Journal articles on the topic 'Student Outcome Prediction'
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Mohd Talib, Nur Izzati, Nazatul Aini Abd Majid, and Shahnorbanun Sahran. "Identification of Student Behavioral Patterns in Higher Education Using K-Means Clustering and Support Vector Machine." Applied Sciences 13, no. 5 (March 3, 2023): 3267. http://dx.doi.org/10.3390/app13053267.
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In many academic fields, predicting student academic success using data mining techniques has long been a major research issue. Monitoring students in higher education institutions (HEIs) and having the ability to predict student performance is important to improve academic quality. The objective of the study is to (1) identify features that form clusters that have holistic characteristics and (2) develop and validate a prediction model for each of the clusters to predict student performance holistically. For this study, both classification and clustering methods will be used using Support Vector Machine (SVM) and K-means clustering. Three clusters were identified using K-means clustering. Based on the learning program outcome feature, there are primarily three types of students: low, average, and high performance. The prediction model with the new labels obtained from the clusters also gained higher accuracy when compared to the student dataset with labels using their semester grade.
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Issaro, Sasitorn, and Panita Wannapiroon. "Intelligent Student Relationship Management Platform with Machine Learning for Student Empowerment." International Journal of Emerging Technologies in Learning (iJET) 18, no. 04 (February 23, 2023): 66–87. http://dx.doi.org/10.3991/ijet.v18i04.32583.
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Students’ grades can affect their future studies at university. The COVID-19 situation has resulted in a greater amount of online teaching, in which teachers and learners rarely interact, causing additional problems with academic performance. This research aims to design and develop an intelligent student relationship management platform (an intelligent SRM platform) using machine learning prediction for student empowerment. This research begins with the synthesis of the factors, the machine learning prediction process, and the platform components. The results of the synthesis establish the design of the platform. Undergraduate students’ grades are then predicted using the decision tree algorithm. Students are divided into two groups, empowerment and non-empowerment groups, using this algorithm. The results show that the learning outcome prediction model has an accuracy of 100.00% and an F-measure of 100%. The most important factor for improving grades is the grade point average, with a weight of 0.637. Therefore, student empowerment to provide students with better grades is essential. This paper presents two approaches to student empowerment: using artificial intelligence technology from the intelligent SRM platform and empowering teachers.
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Roberts, Scott L. "Keep’em Guessing: Using Student Predictions to Inform Historical Understanding and Empathy." Social Studies Research and Practice 11, no. 3 (November 1, 2016): 45–50. http://dx.doi.org/10.1108/ssrp-03-2016-b0004.
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Authors frequently discuss and provide examples of doing history in the social studies classroom. Few focus, however, on allowing students to predict the outcome of historical events before learning what actually happened. In this article, I describe an activity allowing students to make their own predictions informing their understanding of the historical events related to Articles of Confederation. I developed this strategy based on my evolving understanding of how to bring historical thinking into the classroom. I discuss adding the concept of prediction to a previously published lesson plan and how, during my subsequent year in the classroom, I enriched the lesson to elicit student empathy. Finally, the article offers suggestions for teachers developing their own lessons incorporating student predictions.
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Harwati, Defi Sri, and Heri Yanto. "Vocational High School (SMK) Students Accounting Competence Prediction Model by Using Astin I-E-O Model." Dinamika Pendidikan 12, no. 2 (March 1, 2018): 98–113. http://dx.doi.org/10.15294/dp.v12i2.10826.
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This research aims to know the descriptive of input, environment, and outcome; analyze the influence of input to outcome; analyze the influence of environment to outcome; analyze the influence of input to environement; and analyze the role of environment in mediating the influence of input to outcome. Student previous achievement consisting of Mathematics and Indonesian National Exam at Junior High School is the educational input. Student engagement consisting of school engagement and class engagement is the educational environment and student accounting competence is the educational outcome. This research was a quantitative research. Data analysis used descriptive and path analysis technique. The total population and sample consists of 128 students of first class accounting. The results and conclusions in this study indicate that accounting competence is still good, student engagement is good, and student previous achievement is very good. There are influences of mathematics National Exam, Indonesian National Exam, school engagement, and class engagement on student accounting competence; there are influences of Mathematics and the Indonesian National Exam on school engagement and class engagement. School engagement mediates the influence of mathematics National Exam on accounting competence, but it does not mediate the influence of the Indonesian National Exam on accounting competence. Then, class engagement mediates the influences of the mathematics and Indonesian National Exam on accounting competencies.
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P S, Ambili, and Biku Abraham. "A Predictive Model for Student Employability Using Deep Learning Techniques." ECS Transactions 107, no. 1 (April 24, 2022): 10149–58. http://dx.doi.org/10.1149/10701.10149ecst.
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Education in the present scenario is outcome based and focuses mainly on the skill sets a student acquires on completion of the studies. The society is increasingly concerned about the quality of programs, international rankings, and placement statistics of HEI (Higher Education Institutions). This study concentrates on how demographic data, scholastic and co-scholastic abilities of students, faculty characteristics, and teaching practices contribute to the student learning. Dataset pertaining to the study were collected from the same institution for which the placement prediction needs to be calculated. The study models the problem as a sequential event prediction problem and employs deep learning techniques. The proposed model extracts data from dataset with 18 attributes. This predictive approach evaluates the performance of lower level and higher order skills and provide the enhancement methods by which a student can be on the path to full-time employment before leaving the campus.
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Khan, Ijaz Muhammad, Abdul Rahim Ahmad, Nafaa Jabeur, and Mohammed Najah Mahdi. "A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 15 (August 11, 2021): 4. http://dx.doi.org/10.3991/ijim.v15i15.20019.
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One of the important key applications of learning analytics is offering an opportunity to the institutions to track the student’s academic activities and provide them with real-time adaptive consultations if the student academic performance diverts towards the inadequate outcome. Still, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. The machine learning algorithm’s performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of productive attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with the student’s attributes used in developing prediction models. We propose a conceptual framework which demonstrates the classification of attributes as either latent or dynamic. The latent attributes may appear significant but the student is not able to control these attribute, on the other hand, the student has command to restrain the dynamic attributes. Each of the major class is further categorized to present an opportunity to the researchers to pick constructive attributes for model development.
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Ghodke, Keerti. "Stream Processing for Association Rule to Generate Student Dataset using Apriori Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3721–27. http://dx.doi.org/10.22214/ijraset.2022.45884.
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Abstract: Analytical techniques have been used for many years to analyse and predict academic achievement from various perspectives. One of the most challenging problems for higher education is predicting students' paths through the education system. Many factors influence successful student outcome prediction in the early course stage. Apriori algorithm techniques use a variety of methods to find out and collect based on stored data patterns student information. Colab and Python applications are used in this project to predict each student based on characteristics in the given dataset. Each student's information is included in the dataset. Because it arrives as it is being created, received real-world data is referred to that as streaming data.
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Behr, Andreas, Marco Giese, Herve D. Teguim K, and Katja Theune. "Early Prediction of University Dropouts – A Random Forest Approach." Jahrbücher für Nationalökonomie und Statistik 240, no. 6 (February 11, 2020): 743–89. http://dx.doi.org/10.1515/jbnst-2019-0006.
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AbstractWe predict university dropout using random forests based on conditional inference trees and on a broad German data set covering a wide range of aspects of student life and study courses. We model the dropout decision as a binary classification (graduate or dropout) and focus on very early prediction of student dropout by stepwise modeling students’ transition from school (pre-study) over the study-decision phase (decision phase) to the first semesters at university (early study phase). We evaluate how predictive performance changes over the three models, and observe a substantially increased performance when including variables from the first study experiences, resulting in an AUC (area under the curve) of 0.86. Important predictors are the final grade at secondary school, and also determinants associated with student satisfaction and their subjective academic self-concept and self-assessment. A direct outcome of this research is the provision of information to universities wishing to implement early warning systems and more personalized counseling services to support students at risk of dropping out during an early stage of study.
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Pan, Feng, Bingyao Huang, Chunhong Zhang, Xinning Zhu, Zhenyu Wu, Moyu Zhang, Yang Ji, Zhanfei Ma, and Zhengchen Li. "A survival analysis based volatility and sparsity modeling network for student dropout prediction." PLOS ONE 17, no. 5 (May 5, 2022): e0267138. http://dx.doi.org/10.1371/journal.pone.0267138.
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Student Dropout Prediction (SDP) is pivotal in mitigating withdrawals in Massive Open Online Courses. Previous studies generally modeled the SDP problem as a binary classification task, providing a single prediction outcome. Accordingly, some attempts introduce survival analysis methods to achieve continuous and consistent predictions over time. However, the volatility and sparsity of data always weaken the models’ performance. Prevailing solutions rely heavily on data pre-processing independent of predictive models, which are labor-intensive and may contaminate authentic data. This paper proposes a Survival Analysis based Volatility and Sparsity Modeling Network (SAVSNet) to address these issues in an end-to-end deep learning framework. Specifically, SAVSNet smooths the volatile time series by convolution network while preserving the original data information using Long-Short Term Memory Network (LSTM). Furthermore, we propose a Time-Missing-Aware LSTM unit to mitigate the impact of data sparsity by integrating informative missingness patterns into the model. A survival analysis loss function is adopted for parameter estimation, and the model outputs monotonically decreasing survival probabilities. In the experiments, we compare the proposed method with state-of-the-art methods in two real-world MOOC datasets, and the experiment results show the effectiveness of our proposed model.
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Nyompa, Sukri, Suprapta Suprapta, Sri Wahyuni, and Muhamad Ihsan Azhim. "The Effect of Student Perception of Teacher Professional Competency On The Result of Geography Learning Class XI Social Science Student’s SMA 12 Sinjai." UNM Geographic Journal 1, no. 2 (February 1, 2018): 131. http://dx.doi.org/10.26858/ugj.v1i2.6597.
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This research aims to find out: 1 Perceptions of student competency) professional teachers; 2) student learning outcomes; 3) influence the perceptions of students on the professional competence of teachers towards learning outcomes students. This research is the research of ex post facto. Free variables i.e. perception of students on the professional competence of teachers and variable is the result of student learning. Student population of Class XI IPS amounted to 49 students, samples taken 100% is 49 students. The collection of data through observation, question form and the documentation value of Deuteronomy daily student. Data analysis using descriptive analysis and inferensial correlation coefficients of determination of Moment, Product, test data, test the normality and simple linear regression linearity. The results showed that: 1) perceptions of students on the professional competence of teachers having an average score of 3.13 percentage with 78.25% higher categories include; student learning outcome 2) has an average of 3.18 with a percentage of 79.5% categories include enough; 3) inferensial analysis results obtained r_hitung (0.511) greater than r_tabel (0,281) with 5% error level. It can be concluded that the perceptions of students on professional competence teachers influential significantly to student learning outcomes of 26.1% and regression equation Ỷ = 55.639 + 0.476 X can be used in prediction of the level of perception of students on professional competence teachers learn if the result is raised or lowered.
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Zhang, Qizhen, Audrey Durand, and Joelle Pineau. "Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13983–84. http://dx.doi.org/10.1609/aaai.v34i10.7264.
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Applications of machine learning in biomedical prediction tasks are often limited by datasets that are unrepresentative of the sampling population. In these situations, we can no longer rely only on the the training data to learn the relations between features and the prediction outcome. Our method proposes to learn an inductive bias that indicates the relevance of each feature to outcomes through literature mining in PubMed, a centralized source of biomedical documents. The inductive bias acts as a source of prior knowledge from experts, which we leverage by imposing an extra penalty for model weights that differ from this inductive bias. We empirically evaluate our method on a medical prediction task and highlight the importance of incorporating expert knowledge that can capture relations not present in the training data.
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Sullivan, Arthur P., Robert Guglielmo, and Prudence Opperman. "Measuring and Interpreting School-Based Prevention Outcomes: The New York City Model." Journal of Drug Education 16, no. 2 (June 1986): 181–90. http://dx.doi.org/10.2190/4mfb-2u39-3u50-ncpv.
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Theoretical standpoint, procedures and instruments used to evaluate school-based substance abuse prevention in the New York City public schools are detailed. Outcome measures are discussed, and the argument is made that the process by which the outcome behavioral change was achieved must be explored before the outcomes are certified as beneficial and appropriate for an educational environment. The changes in the meanings the student attaches to objects and events in his environment and the way in which he construes his environment and self which are antecedent to behavioral change are explored by obtaining written responses from the student, but more fully by repeated observation of the prevention process for the duration of the prevention activities. If the changes in meaning and construct are likely to enhance the student's life, the outcome behaviors are judged adequate. A further argument is made that prediction of future behaviors can be made from the meaning and construct data, and these favorable or unfavorable predictions caused by changes attributable to prevention activities can serve as a basis for evaluation of the prevention work, even in the absence of presently observable behavioral outcomes.
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Son, Nguyen Thi Kim, Nguyen Van Bien, Nguyen Huu Quynh, and Chu Cam Tho. "Machine Learning Based Admission Data Processing for Early Forecasting Students' Learning Outcomes." International Journal of Data Warehousing and Mining 18, no. 1 (January 1, 2022): 1–15. http://dx.doi.org/10.4018/ijdwm.313585.
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In this paper, the authors explore the factors to improve the accuracy of predicting student learning outcomes. The method can remove redundant and irrelevant factors to get a “clean” data set without having to solve the NP-Hard problem. The method can improve the graduation outcome prediction accuracy through logistic regression machine learning method for “clean” data set. They empirically evaluate the training and university admission data of Hanoi Metropolitan University from 2016 to 2020. From data processing results and the support from the machine learning techniques application program, they analyze, evaluate, and forecast students' learning outcomes based on admission data, first-year, and second-year academic performance data. They then submit proposals of training and admission policies and methods of radically and quantitatively solving problems in university admissions.
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Alhothali, Areej, Maram Albsisi, Hussein Assalahi, and Tahani Aldosemani. "Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A Review." Sustainability 14, no. 10 (May 19, 2022): 6199. http://dx.doi.org/10.3390/su14106199.
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Recent years have witnessed an increased interest in online education, both massive open online courses (MOOCs) and small private online courses (SPOCs). This significant interest in online education has raised many challenges related to student engagement, performance, and retention assessments. With the increased demands and challenges in online education, several researchers have investigated ways to predict student outcomes, such as performance and dropout in online courses. This paper presents a comprehensive review of state-of-the-art studies that examine online learners’ data to predict their outcomes using machine and deep learning techniques. The contribution of this study is to identify and categorize the features of online courses used for learners’ outcome prediction, determine the prediction outputs, determine the strategies and feature extraction methodologies used to predict the outcomes, describe the metrics used for evaluation, provide a taxonomy to analyze related studies, and provide a summary of the challenges and limitations in the field.
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Ismail Yusuf Panessai, Muhammad Modi Lakulu, Mohd Hishamuddin Abdul Rahman, Noor Anida Zaria Mohd Noor, Nor Syazwani Mat Salleh, and Aldrin Aran Bilong. "PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5." International Journal of Artificial Intelligence 6, no. 2 (December 3, 2019): 125–33. http://dx.doi.org/10.36079/lamintang.ijai-0602.42.
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PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5
This study was aimed to increase the performance of the Predicting Student Academic Performance (PSAP) system, and the outcome is to develop a web application that can be used to analyze student performance during present semester. Development of the web-based application was based on the evolutionary prototyping model. The study also analyses the accuracy of the classifier that is constructed for the prediction features in the web application. Qualitative approaches by user evaluation questionnaire were used for this study. A number of few personnel expert users which are lecturers from Universiti Pendidikan Sultan Idris were chosen as respondents. Each respondent is instructed to answer a total of 27 questions regarding respondent’s background and web application design. The accuracy of the classifier for the prediction features is tested by using the confusion matrix by using the test set of 24 rows. The findings showed the views of respondents on the aspects of interface design, functionality, navigation, and reliability of the web-based application that is developed. The result also showed that accuracy for the classifier constructed by using ID3 classification model (C4.5) is 79.18% and the highest compared to Naïve Bayes and Generalized Linear classification model.
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Siregar, Nurhasana, Rodiah Ulfah Lubis, and Puspa Riani Nasution. "Student Practicum Competencies through Lesson Study with the application of Argument Driven Inquiry." Jurnal Pembelajaran Fisika 9, no. 2 (June 30, 2019): 243–51. http://dx.doi.org/10.23960/jpf.v9.n2.202111.
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This study intends to analyse the student's practicum competencies through Lesson Study activities by applying the Argument Driven Inquiry learning model which was studied using qualitative research with descriptive qualitative analysis techniques. The sample is 13 students in the fourth semester of physics education. Based on students’ practicum reports, the percentage of learning outcomes in the first Lesson Study was 70.91, the second Lesson Study was 84.83. The indicators of student practicum competencies include the competencies of synthesis, cooperation, communication and independence, each percentage gain is 41.03%; 47.12%; 54.49%; 45.77% in Lesson Study 1, and 77.56%; 82.69%; 78.85%; 76.92% in Lesson Study 2. Student responses to practicum competencies for each indicator are 80.13%; 83.33%; 82.05%; 78.21%. These show that there is a positive change in student practicum competencies and the reflection’s results show that there is an accurate prediction of the final measurement result and knowing how to obtain the right measurement outcome.
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Cronin, Christopher. "Reasons for Drinking Versus Outcome Expectancies in the Prediction of College Student Drinking." Substance Use & Misuse 32, no. 10 (January 1997): 1287–311. http://dx.doi.org/10.3109/10826089709039379.
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Owusu-Boadu, Bridgitte, Isaac Kofi Nti, Owusu Nyarko-Boateng, Justice Aning, and Victoria Boafo. "Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Features." Applied Computer Systems 26, no. 2 (December 1, 2021): 122–31. http://dx.doi.org/10.2478/acss-2021-0015.
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Abstract The academic performance of students is essential for academic progression at all levels of education. However, the availability of several cognitive and non-cognitive factors that influence students’ academic performance makes it challenging for academic authorities to use conventional analytical tools to extract hidden knowledge in educational data. Therefore, Educational Data Mining (EDM) requires computational techniques to simplify planning and determining students who might be at risk of failing or dropping from school due to academic performance, thus helping resolve student retention. The paper studies several cognitive and non-cognitive factors such as academic, demographic, social and behavioural and their effect on student academic performance using machine learning algorithms. Heterogenous lazy and eager machine learning classifiers, including Decision Tree (DT), K-Nearest-Neighbour (KNN), Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), AdaBoost and Support Vector Machine (SVM) were adopted and training was performed based on k-fold (k = 10) and leave-one-out cross-validation. We evaluated their predictive performance using well-known evaluation metrics like Area under Curve (AUC), F-1 score, Precision, Accuracy, Kappa, Matthew’s correlation coefficient (MCC) and Recall. The study outcome shows that Student Absence Days (SAD) are the most significant predictor of students’ academic performance. In terms of prediction accuracy and AUC, the RF (Acc = 0.771, AUC = 0.903), LR (Acc = 0.779, AUC = 0.90) and ANN (Acc = 0.760, AUC = 0.895) outperformed all other algorithms (KNN (Acc = 0.638, AUC = 0.826), SVM (Acc = 0.727, AUC = 0.80), DT (Acc = 0.733, AUC = 0.876) and AdaBoost (Acc = 0.748, AUC = 0.808)), making them more suitable for predicting students’ academic performance.
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Modell, H. I., J. A. Michael, T. Adamson, J. Goldberg, B. A. Horwitz, D. S. Bruce, M. L. Hudson, S. A. Whitescarver, and S. Williams. "Helping undergraduates repair faulty mental models in the student laboratory." Advances in Physiology Education 23, no. 1 (June 2000): S82–90. http://dx.doi.org/10.1152/advances.2000.23.1.s82.
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Over half of the undergraduate students entering physiology hold a misconception concerning how breathing pattern changes when minute ventilation increases. Repair of this misconception was used as a measure to compare the impact of three student laboratory protocols on learning by 696 undergraduate students at 5 institutions. Students were tested for the presence of the misconception before and after performing a laboratory activity in which they measured the effect of exercise on tidal volume and breathing frequency. The first protocol followed a traditional written "observe and record" ("cookbook") format. In the second treatment group, a written protocol asked students to complete a prediction table before running the experiment ("predictor" protocol). Students in the third treatment group were given the written "predictor" protocol but were also required to verbalize their predictions before running the experiment ("instructor intervention" protocol). In each of the three groups, the number of students whose performance improved on the posttest was greater than the number of students who performed less well on the posttest (P < 0.001). Thus the laboratory protocols helped students correct the misconception. However, the remediation rate for students in the "instructor intervention" group was more than twice that observed for the other treatment groups (P < 0.001). The results indicate that laboratory instruction is more effective when students verbalize predictions from their mental models than when they only "discover" the outcome of the experiment.
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Subhash, Ambika Rani. "Student Campus Placement Prediction Analysis using ChiSquared Test on Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 427–34. http://dx.doi.org/10.22214/ijraset.2021.37368.
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Every higher education institute aims to provide the best career opportunities for their students as part of the outcome based education system. In India, campus placements for students while pursuing their 4th year of engineering is a predominant factor since the reputation of any institute largely depends on reputed recruiting companies visiting campus and the number of placement offers being given to eligible students. Hence, campuses offer personality development training to their students just before the commencement of the placement season while students try to maintain a minimum CGPA which would ensure their eligibility to apply for companies of their choice. The purpose of this paper is to predict a student’s chances of obtaining a pre-placement offer while still in campus on the basis of various academic and non-academic factors. The dataset used for the prediction analysis consists of student related aspects such as their university seat numbers, academic grades and personality training parameters. The training models have been designed using the WEKA tool and in addition to supervised machine learning classification algorithms, Chi-squared tests has been implemented on the dataset to only obtain those attributes that might be the highest requirement for campus placements of students.
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Jawthari, Moohanad, and Veronika Stoffa. "Predicting At-Risk Students Using Weekly Activities and Assessments." International Journal of Emerging Technologies in Learning (iJET) 17, no. 19 (October 14, 2022): 59–73. http://dx.doi.org/10.3991/ijet.v17i19.31349.
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Abstract— Although Online learning has been so popular especially during epidemic crisis, it has a drawback of high dropouts and low completion rates. Institutes search for ways to support their students learning and increase completion rates. Institutes will be able to predict students’ performances and make interventions on time if they have some analytical strategy. Yet, efficient prediction and proactive intervention depends on using meaningful, reliable, and accurate data. Institutes different tools like Virtual Learning Environment (VLE) for teaching and content delivery. These tools provide large databases that are useful to improve prediction of students’ performance research. In this study, an Open University course VLE data is analyzed to investigate if weekly engagement alone, integrated with assessments scores (first approach), and accumulated previous assessments up to a certain week data ((second approach) lead to accurate student performance prediction. Importance of VLE data is highlighted here, which sheds light on students’ haviour and leads to developing models that can predict student’s outcome accurately. Second approach generated robust prediction models which outperformed the results obtained using first approach.
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Nadaf, Ali, Sebas Eliëns, and Xin Miao. "Interpretable-Machine-Learning Evidence for Importance and Optimum of Learning Time." International Journal of Information and Education Technology 11, no. 10 (2021): 444–49. http://dx.doi.org/10.18178/ijiet.2021.11.10.1548.
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This study uses a machine learning technique, a boosted tree model, to relate the student cognitive achievement in the 2018 data from the Programme of International Student Assessment (PISA) to other features related to the student learning process, capturing the complex and nonlinear relationships in the data. The SHapley Additive exPlanations (SHAP) approach is subsequently used to explain the complexity of the model. It reveals the relative importance of each of the features in predicting cognitive achievement. We find that instruction time comes out as an important predictor, but with a nonlinear relationship between its value and the contribution to the prediction. We find that a large weekly learning time of more than 35 hours is associated with less positive or even negative effect on the predicted outcome. We discuss how this method can possibly be used to signal problems in the student population related to learning time or other features.
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Joksimović, Srećko, Oleksandra Poquet, Vitomir Kovanović, Nia Dowell, Caitlin Mills, Dragan Gašević, Shane Dawson, Arthur C. Graesser, and Christopher Brooks. "How Do We Model Learning at Scale? A Systematic Review of Research on MOOCs." Review of Educational Research 88, no. 1 (November 14, 2017): 43–86. http://dx.doi.org/10.3102/0034654317740335.
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Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models.
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Paliwal, Nikhil, Prakhar Jaiswal, Vincent M. Tutino, Hussain Shallwani, Jason M. Davies, Adnan H. Siddiqui, Rahul Rai, and Hui Meng. "Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning." Neurosurgical Focus 45, no. 5 (November 2018): E7. http://dx.doi.org/10.3171/2018.8.focus18332.
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OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment hemodynamics, and FD-device characteristics and then used these parameters to train machine learning algorithms to predict 6-month clinical outcomes after FD treatment.METHODSData were retrospectively collected for 84 FD-treated sidewall aneurysms in 80 patients. Based on 6-month angiographic outcomes, IAs were classified as occluded (n = 63) or residual (incomplete occlusion, n = 21). For each case, the authors modeled FD deployment using a fast virtual stenting algorithm and hemodynamics using image-based computational fluid dynamics. Sixteen morphological, hemodynamic, and FD-based parameters were calculated for each aneurysm. Aneurysms were randomly assigned to a training or testing cohort in approximately a 3:1 ratio. The Student t-test and Mann-Whitney U-test were performed on data from the training cohort to identify significant parameters distinguishing the occluded from residual groups. Predictive models were trained using 4 types of supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM; linear and Gaussian kernels), K-nearest neighbor, and neural network (NN). In the testing cohort, the authors compared outcome prediction by each model trained using all parameters versus only the significant parameters.RESULTSThe training cohort (n = 64) consisted of 48 occluded and 16 residual aneurysms and the testing cohort (n = 20) consisted of 15 occluded and 5 residual aneurysms. Significance tests yielded 2 morphological (ostium ratio and neck ratio) and 3 hemodynamic (pre-treatment inflow rate, post-treatment inflow rate, and post-treatment aneurysm averaged velocity) discriminants between the occluded (good-outcome) and the residual (bad-outcome) group. In both training and testing, all the models trained using all 16 parameters performed better than all the models trained using only the 5 significant parameters. Among the all-parameter models, NN (AUC = 0.967) performed the best during training, followed by LR and linear SVM (AUC = 0.941 and 0.914, respectively). During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome.CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.
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Ehimwenma, Kennedy Efosa, Safiya Al Sharji, and Maruf Raheem. "Difference of Probability and Information Entropy for Skills Classification and Prediction in Student Learning." International Journal of Artificial Intelligence & Applications 13, no. 5 (September 30, 2022): 1–19. http://dx.doi.org/10.5121/ijaia.2022.13501.
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The probability of an event is in the range of [0, 1]. In a sample space S, the value of probability determines whether an outcome is true or false. The probability of an event Pr(A) that will never occur = 0. The probability of the event Pr(B) that will certainly occur = 1. This makes both events A and B thus a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + … + Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. Firstly, this paper discusses Bayes’ theorem, then complement of probability and the difference of probability for occurrences of learning-events, before applying these in the prediction of learning objects in student learning. Given the sum total of 1; to make recommendation for student learning, this paper submits that the difference of argMaxPr(S) and probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates: i) the probability of skill-set events that has occurred that would lead to higher level learning; ii) the probability of the events that has not occurred that requires subject-matter relearning; iii) accuracy of decision tree in the prediction of student performance into class labels; and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning [1].
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Balaji, Prasanalakshmi, Salem Alelyani, Ayman Qahmash, and Mohamed Mohana. "Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review." Applied Sciences 11, no. 21 (October 26, 2021): 10007. http://dx.doi.org/10.3390/app112110007.
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Machine learning is emerging nowadays as an important tool for decision support in many areas of research. In the field of education, both educational organizations and students are the target beneficiaries. It facilitates the educational sector in predicting the student’s outcome at the end of their course and for the students in deciding to choose a suitable course for them based on their performances in previous exams and other behavioral features. In this study, a systematic literature review is performed to extract the algorithms and the features that have been used in the prediction studies. Based on the search criteria, 2700 articles were initially considered. Using specified inclusion and exclusion criteria, quality scores were provided, and up to 56 articles were filtered for further analysis. The utmost care was taken in studying the features utilized, database used, algorithms implemented, and the future directions as recommended by researchers. The features were classified as demographic, academic, and behavioral features, and finally, only 34 articles with these features were finalized, whose details of study are provided. Based on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability to predict the students’ performance based on specified features as categorized and can be used by students as well as academic institutions. A specific machine learning model identification for the purpose of student academic performance prediction would not be feasible, since each paper taken for review involves different datasets and does not include benchmark datasets. However, the application of the machine learning techniques in educational mining is still limited, and a greater number of studies should be carried out in order to obtain well-formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.
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Kamagi, David Hartanto, and Seng Hansun. "Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa." Jurnal ULTIMATICS 6, no. 1 (June 1, 2014): 15–20. http://dx.doi.org/10.31937/ti.v6i1.327.
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Graduation Information is important for Universitas Multimedia Nusantara which engaged in education. The data of graduated students from each academic year is an important part as a source of information to make a decision for BAAK (Bureau of Academic and Student Administration). With this information, a prediction can be made for students who are still active whether they can graduate on time, fast, late or drop out with the implementation of data mining. The purpose of this study is to make a prediction of students’ graduation with C4.5 algorithm as a reference for making policies and actions of academic fields (BAAK) in reducing students who graduated late and did not pass. From the research, the category of IPS semester one to semester six, gender, origin of high school, and number of credits, can predict the graduation of students with conditions quickly pass, pass on time, pass late and drop out, using data mining with C4.5 algorithm. Category of semester six is the highly influential on the predicted outcome of graduation. With the application test result, accuracy of the graduation prediction acquired is 87.5%.
Index Terms-Data mining, C4.5 algorithm, Universitas Multimedia Nusantara, prediction.
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Karlos, Stamatis, Georgios Kostopoulos, and Sotiris Kotsiantis. "Predicting and Interpreting Students’ Grades in Distance Higher Education through a Semi-Regression Method." Applied Sciences 10, no. 23 (November 26, 2020): 8413. http://dx.doi.org/10.3390/app10238413.
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Multi-view learning is a machine learning app0roach aiming to exploit the knowledge retrieved from data, represented by multiple feature subsets known as views. Co-training is considered the most representative form of multi-view learning, a very effective semi-supervised classification algorithm for building highly accurate and robust predictive models. Even though it has been implemented in various scientific fields, it has not adequately used in educational data mining and learning analytics, since the hypothesis about the existence of two feature views cannot be easily implemented. Some notable studies have emerged recently dealing with semi-supervised classification tasks, such as student performance or student dropout prediction, while semi-supervised regression is uncharted territory. Therefore, the present study attempts to implement a semi-regression algorithm for predicting the grades of undergraduate students in the final exams of a one-year online course, which exploits three independent and naturally formed feature views, since they are derived from different sources. Moreover, we examine a well-established framework for interpreting the acquired results regarding their contribution to the final outcome per student/instance. To this purpose, a plethora of experiments is conducted based on data offered by the Hellenic Open University and representative machine learning algorithms. The experimental results demonstrate that the early prognosis of students at risk of failure can be accurately achieved compared to supervised models, even for a small amount of initially collected data from the first two semesters. The robustness of the applying semi-supervised regression scheme along with supervised learners and the investigation of features’ reasoning could highly benefit the educational domain.
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Mansouri, Taha, Ahad ZareRavasan, and Amir Ashrafi. "A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance." Journal of Information Technology Education: Research 20 (2021): 221–43. http://dx.doi.org/10.28945/4760.
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Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology: Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student’s overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution: This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings: The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students’ total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student’s overall performance. Recommendations for Practitioners: Academic institutions can use the results and approach developed in this paper to identify students’ performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendation for Researchers: Researchers can use the proposed approach in this research to deal with the problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society: Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research: As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model’s elements.
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Axelsen, Megan, Petrea Redmond, Eva Heinrich, and Michael Henderson. "The evolving field of learning analytics research in higher education." Australasian Journal of Educational Technology 36, no. 2 (May 15, 2020): 1–7. http://dx.doi.org/10.14742/ajet.6266.
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Over the last decade the deployment and use of learning analytics has become routine in many universities around the world. The ability to analyse the way students interact with technology has demonstrated significant value for providing insights into student learning and there are now a wide range of uses for learning analytics in education. From use as a diagnostic tool, to a method for prediction, learning analytics in higher education has an emphasis on a wide range of outcome measures, including student retention, progression, attainment, performance, mastery, employability and engagement. In exploring how learning analytics can improve learning practice by transforming the ways we support learning processes, this editorial highlights some of the learning analytics research that has been published in AJET to date.
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Khan, Ijaz Muhammad, Abdul Rahim Ahmad, Nafaa Jabeur, and Mohammed Najah Mahdi. "Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course." International Journal of Emerging Technologies in Learning (iJET) 16, no. 17 (September 6, 2021): 42. http://dx.doi.org/10.3991/ijet.v16i17.18995.
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The new students struggle to understand the introductory programming courses, due to its intricate nature, which results in higher dropout and increased failure rates. Despite implementing productive methodologies, the instructor struggles to identify the students with distinctive levels of skills. The modern institutes are looking for technology-equipped practices to classify the students and prepare personalized consultation procedures for each class. This paper applies decision tree-based machine learning classifiers to develop a prediction model competent to forecast the outcome of the introductory programming students at an early stage of the semester. The model is then transformed into an adaptive consultation framework which generates three types of colored signals; red, yellow, and green which illustrates whether the student is performing low, average, or high respectively. This provides an opportunity for the instructor to set precautionary measures for low performing students and set complicated tasks that help the highly skilled students to improve their skills further. The experiments compare a set of decision tree-based classifiers and conclude J48 as an efficient model in classifying students in all classes with high accuracy, sensitivity, and F-measure. Even though the aim of the research is to focus on introductory programming courses, however, the framework is flexible and can be implemented in other courses.
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Sharma, Sneha, and Raman Tandon. "Predicting Burn Mortality Using a Simple Novel Prediction Model." Indian Journal of Plastic Surgery 54, no. 01 (January 2021): 046–52. http://dx.doi.org/10.1055/s-0040-1721867.
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Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.
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Graefe, Andreas, and Christof Weinhardt. "LONG-TERM FORECASTING WITH PREDICTION MARKETS – A FIELD EXPERIMENT ON APPLICABILITY AND EXPERT CONFIDENCE." Journal of Prediction Markets 2, no. 2 (December 14, 2012): 71–91. http://dx.doi.org/10.5750/jpm.v2i2.442.
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While prediction markets have become increasingly popular to forecast the near-term future, the literature provides little evidence on how they perform for long-term problems. For assessing the long-term, decision-makers traditionally rely on experts, although empirical research disputes the value of expert advice. Reporting on findings from a field experiment in which we implemented two prediction markets in parallel to a Delphi study, this paper addresses two questions. First, we analyze the applicability of prediction markets for long-term problems whose outcome cannot be judged for a long time. Second, by comparing trading behavior of an expert and a student market, we analyze whether there is evidence that supports the assumption that experts possess superior knowledge. Our results show that prediction markets provide similar results as the well-established Delphi method. We conclude that prediction markets appear to be applicable for long-term forecasting. Furthermore, we observe differences in the confidence of experts and non-experts. Our findings indicate that, in contrast to students, experts reveal their information well-considered based on what they think they know. Finally, we discuss how such analyses of market participants’ confidence provide valuable information to decision-makers and may be used to improve on traditional forecasting methods.
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Xie, Shu-tong, Qiong Chen, Kun-hong Liu, Qing-zhao Kong, and Xiu-juan Cao. "Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses." Scientific Programming 2021 (June 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/9977977.
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In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year’s epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the “College Information Technology Fundamentals” course of three colleges to conduct student learning behavior analysis and learning outcome prediction. Firstly, data collection and preprocessing are carried out; cluster analysis is performed by using k-means algorithms. Four typical learning behavior patterns have been obtained from previous research, and these patterns were analyzed in terms of teaching videos, quizzes, and platform visits. Secondly, a multiclass classification framework, which combines a feature selection method based on genetic algorithm (GA) with the error correcting output code (ECOC) method, is designed for training the classification model to achieve the prediction of grade levels of students. The experimental results show that the multiclass classification method proposed in this paper can effectively predict the grade of performance, with an average accuracy rate of over 75%. The research results help to implement personalized teaching for students with different grades and learning patterns.
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Lee, Kibeom, Michael C. Ashton, Jocelyn Wiltshire, Joshua S. Bourdage, Beth A. Visser, and Alissa Gallucci. "Sex, Power, and Money: Prediction from the Dark Triad and Honesty–Humility." European Journal of Personality 27, no. 2 (March 2013): 169–84. http://dx.doi.org/10.1002/per.1860.
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Data were collected from two undergraduate student samples to examine (i) the relations of the ‘Dark Triad’ variables (Machiavellianism, Psychopathy, and Narcissism) with the HEXACO personality dimensions, as well as (ii) the ability of the aforementioned characteristics and of the Big Five personality factors to predict outcome variables related to sex, power, and money. Results indicated that the common variance of the Dark Triad was very highly correlated with low Honesty–Humility and that the unique variance of each of the Dark Triad variables also showed theoretically meaningful relations with the other five HEXACO factors. Furthermore, the Dark Triad and Honesty–Humility were strong predictors of three domains of outcome variables—Sex (short–term mating tendencies and sexual quid pro quos), Power (Social Dominance Orientation and desire for power), and Money (conspicuous consumption and materialism)—that were not well predicted by the dimensions of the Big Five. Copyright © 2012 John Wiley & Sons, Ltd.
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Bruce, Scott L., Elizabeth Crawford, Gary B. Wilkerson, David Rausch, R. Barry Dale, and Martina Harris. "Prediction Modeling for Academic Success in Professional Master's Athletic Training Programs." Athletic Training Education Journal 11, no. 4 (October 1, 2016): 194–207. http://dx.doi.org/10.4085/1104194.
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Context: A common goal of professional education programs is to recruit the students best suited for the professional career. Selection of students can be a difficult process, especially if the number of qualified candidates exceeds the number of available positions. The ability to predict academic success in any profession has been a challenging proposition. No studies to date have examined admission predictors of professional master's athletic training programs (PMATP). Objective: The purpose of this study was to identify program applicant characteristics that are most likely to predict academic success within a PMATP. Design: Cohort-based. Setting: University professional PMATP. Patients or Other Participants: A cohort of 119 students who attended a PMATP for at least 1 year. Intervention(s): Common application data from subjects' applications to the university and the PMATP were gathered and used to create the prediction models. Main Outcome Measure(s): Sensitivity, specificity, odds ratio, and relative frequency of success were used to determine the strongest set of predictors. Results: Multiple logistic regression analyses yielded a 3-factor model for prediction of success in the PMATP (undergraduate grade point average ≥ 3.18; Graduate Record Examination quantitative [percentile rank] ≥ 141.5 [≥12]; taking calculus as an undergraduate). A student with ≥2 predictors had an odds ratio of 17.94 and a relative frequency of success of 2.13 for being successful in the PMATP. This model correctly predicted 90.5% of PMATP success. Conclusions: It is possible to predict academic success in a PMATP based on common application data.
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Mbise, E. R., and R. S. J. Tuninga. "Measuring business schools’ service quality in an emerging market using an extended SERVQUAL instrument." South African Journal of Business Management 47, no. 1 (March 31, 2016): 61–74. http://dx.doi.org/10.4102/sajbm.v47i1.53.
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An extended SERVQUAL instrument is developed, validated and used to measure perceived service quality delivered to students by business schools in an emerging market economy. A longitudinal survey is conducted with selected students in their final year of study from two business schools in an emerging market economy. The use of the extended SERVQUAL model is suggested to monitor student/employee expectations and perceptions during and after the education service delivery process. Students attach different weights to the service quality dimensions. A new Process Outcome dimension is found to substantially add to the SERVQUAL model and is more important than the other dimensions. The validity of the extended SERVQUAL model for practical use is α >0.95. Prediction of the level of service quality delivered, using four dimensions, indicates that the level of service quality is explained mostly by Process Outcome and Tangibles dimensions. It is suggested that using the extended SERVQUAL model as a tool can enable managers of business schools to identify the factors on which students/employees base their quality assessment of the education services they receive. Knowledge of these factors will enable managers in emerging economies to periodically assess, sustain and improve quality of the whole service delivery process. Priorities can be set to allocate scarce resources properly to make effective investment decisions to improve quality per school and in higher education, in general. The paper further suggests that regulatory bodies make use of this model when comparing performance of business schools, focusing on student experiences as a supplement to the traditional performance measures.
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Khechine, Hager, and Sawsen Lakhal. "Technology as a Double-Edged Sword: From Behavior Prediction with UTAUT to Students’ Outcomes Considering Personal Characteristics." Journal of Information Technology Education: Research 17 (2018): 063–102. http://dx.doi.org/10.28945/4022.
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Aim/Purpose: We aim to bring a better understanding of technology use in the educational context. More specifically, we investigate the determinants of webinar acceptance by university students and the effects of this acceptance on students’ outcomes in the presence of personal characteristics such as anxiety, attitude, computer self-efficacy, and autonomy. Background: According to literature in information systems, understanding the determinants of technology use and their effect on outcomes can help ensure their effective deployment, which might yield productivity payoffs. Methodology: Data collection with an online quantitative questionnaire yielded to 377 valid responses from students enrolled in an undergraduate management information systems course. SPSS software allowed obtaining descriptive statistics and Smart-PLS was used for validity and hypotheses testing. Contribution: Previous studies assessed either the determinants of technology use or the effect of their use on students’ outcomes, and often omitted to assess the role of personal characteristics. This research fulfills the gap about the scarcity of studies that link goals to intentions and behavior, while considering personal cognitive characteristics. Findings: Results showed that performance expectancy, social influence, facilitating conditions, and voluntariness of use explained the behavioral intention and webinar usage. Some of these relationships were direct and others were moderated. Satisfaction was the only student outcome affected by the use of webinars. Anxiety, attitude, and autonomy are the personal characteristics that exerted direct and moderating effects on the relationships between the main variables of the research model. Recommendations for Practitioners: Results gave rise to interesting managerial recommendations for adopting technologies in universities. Among them, teachers are encouraged to promote the webinars’ advantages and to exert less pressure on students to use webinars. Recommendation for Researchers: On the theoretical side, we brought a holistic view of the use of technologies in higher education by linking goals to intentions and behavior, and integrating personal cognitive characteristics into the same model. Results allowed enriching the literature about technology adoption in the educational context. Future Research: Future research should follow closely the results of studies on generation Z to find better explanatory variables of technology adoption. We also propose to consider new variables from the updated technology acceptance models to further understand the derteminants of technology use by students.
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Grebener, Binia-Laureen, Janina Barth, Sven Anders, Tim Beißbarth, and Tobias Raupach. "A prediction-based method to estimate student learning outcome: Impact of response rate and gender differences on evaluation results." Medical Teacher 43, no. 5 (January 27, 2021): 524–30. http://dx.doi.org/10.1080/0142159x.2020.1867714.
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Soni, Tanu, and Priyadarshini Tiwari. "Predictors of maternal outcome in women on mechanical ventilation in an obstetric intensive care unit: an observational study." International Journal of Reproduction, Contraception, Obstetrics and Gynecology 8, no. 2 (January 25, 2019): 721. http://dx.doi.org/10.18203/2320-1770.ijrcog20190312.
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Background: Present study was designed to note the indications for and the complications and outcome of women on mechanical ventilation in our obstetric intensive care unit, and in addition to look for the applicability and correlation of Sequential Organ Failure Assessment (SOFA) scores for the prediction of outcome in these women.Methods: A prospective observational study was conducted in the obstetric intensive care unit of our teaching hospital which included all women requiring mechanical ventilation in the study period. The diagnosis of the woman on admission, the clinical course and outcome along with total maximum sequential organ failure assessment (SOFA) score and SOFA score for each system were noted. Women were divided into two groups, survivors and non-survivors. Student t test and chi square test were used for analysis.Results: The foremost indication for mechanical ventilation was hypertension in pregnancy namely eclampsia and pre-eclampsia, followed by obstetric hemorrhage and then by hepatic failure. Maternal mortality rose significantly as the number of days of mechanical ventilation increased (p value <0.05). The total SOFA score correlated highly significantly with the outcome (p<0.0001).Conclusions: In women with eclampsia and pre-eclampsia suffering from respiratory failure, survival is inversely correlated with the number of days of mechanical ventilation. The total SOFA score is highly predictive of the woman’s outcome and all individual organ system scores also significantly correlate with outcome except for the score of coagulation system.
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Ellington, Roni, James Wachira, and Asamoah Nkwanta. "RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students." CBE—Life Sciences Education 9, no. 3 (September 2010): 348–56. http://dx.doi.org/10.1187/cbe.10-03-0036.
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The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems.
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Wan, Qing, and Yoonsuck Choe. "Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13945–46. http://dx.doi.org/10.1609/aaai.v34i10.7245.
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Consider a natural language sentence describing a specific step in a food recipe. In such instructions, recognizing actions (such as press, bake, etc.) and the resulting changes in the state of the ingredients (shape molded, custard cooked, temperature hot, etc.) is a challenging task. One way to cope with this challenge is to explicitly model a simulator module that applies actions to entities and predicts the resulting outcome (Bosselut et al. 2018). However, such a model can be unnecessarily complex. In this paper, we propose a simplified neural network model that separates action recognition and state change prediction, while coupling the two through a novel loss function. This allows learning to indirectly influence each other. Our model, although simpler, achieves higher state change prediction performance (67% average accuracy for ours vs. 55% in (Bosselut et al. 2018)) and takes fewer samples to train (10K ours vs. 65K+ by (Bosselut et al. 2018)).
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Narayanasamy, Senthil Kumar, and Atilla Elçi. "An Effective Prediction Model for Online Course Dropout Rate." International Journal of Distance Education Technologies 18, no. 4 (October 2020): 94–110. http://dx.doi.org/10.4018/ijdet.2020100106.
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Due to tremendous reception on digital learning platforms, many online users tend to register for online courses in MOOC offered by many prestigious universities all over the world and gain a lot on cutting edge technologies in niche courses. As the reception of online courses is increasing on one side, there have been huge dropouts of participants in the online courses causing serious problems for the course owners and other MOOC administrators. Hence, it is deemed necessary to find out the root causes of course dropouts and need to prepare a workable solution to prevent that outcome in the future. In this connection, the authors made use of three machine learning algorithms such as support vector machine, random forest, and conditional random fields. The huge samples of datasets were downloaded from the Open University of China, that is, almost 7K student profiles were extracted for the empirical analysis. The datasets were loaded into a confusion matrix and analyzed for the accuracy, precision, recall, and f-score of the model.
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K., Ravi, Vinay K., and Akhila Rao K. "Study of spectrum of sepsis and prediction of its outcome in patients admitted to ICU using different scoring systems." International Journal of Advances in Medicine 6, no. 1 (January 23, 2019): 155. http://dx.doi.org/10.18203/2349-3933.ijam20190123.
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Background: Although sepsis is one of the leading causes of mortality in hospitalized patients, information regarding early predictive factors for mortality and morbidity is limited. The aim was to identify reliable and early prognostic variables predicting mortality in patients admitted to ICU with sepsis.Methods: Patients fulfilling the Surviving Sepsis Campaign 2012 guidelines criteria for sepsis within the ICU were included over two years. Apart from baseline haematological, biochemical and metabolic parameters, APACHE II, SAPS II and SOFA scores were calculated on day 1 of admission. Patients were followed till death or discharge from the ICU. Chi-square test, student t-test, receiver operating curve analyses were done.Results: 100 patients were enrolled during the study period. The overall mortality was 35% (68.6% in males and 31.4% in females). Mortality was 88.6% and 11.4% in patients with septic shock and severe sepsis and none in the sepsis group, respectively. On multivariate analysis, significant predictors of mortality were APACHE II score greater than 27, SAPS II score greater than 43 and SOFA score greater than 11 on day the of admission. On ROC analysis APACHE II had the highest sensitivity (92.3%) and SAPS II had the highest specificity (82.9%).Conclusions: All three scores performed well in predicting the mortality. Overall, APACHE II had highest sensitivity, hence was the best predictor of mortality in critically ill patients. SAPS II had the highest specificity, hence it predicted improvement better than death. SOFA had intermediate sensitivity and specificity.
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Verma, Sneh Lata, Rigzing Lepcha, Rohit Khanna, Tripti Tikku, Rana Pratap Maurya, and Kamna Srivastava. "Comparision of predicted and actual treatment outcome based on steiner cephalometric analysis using nemotech software." IP Indian Journal of Orthodontics and Dentofacial Research 8, no. 3 (October 15, 2022): 151–55. http://dx.doi.org/10.18231/j.ijodr.2022.026.
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Social and psychological concerns, improved function, appearance, and self-esteem encourages a patient to pursue Orthodontic treatment, for which extraction may or may not be needed.Conventionally steiner stick analysis or tweed head plate correction is followed to decide extraction during fixed orthodontic treatment for desirable treatment outcome. This study was designed to compare predicted position and angulation of maxillary and mandibular incisor by steiner stick analysis with the actual treatment outcome using memotech software.Sample was taken from our department consisting of pre and post treatment lateral cephalogram of 15 subject with age ranging from 20- 22years. Tracing was done using Nemotech software and values for Steiner’s analysis was obtained, both for, pre and post treatment tracing. Prediction was done by Steiner Stick analysis(SSA) on the pre treatment tracing.The position and angulation of Maxillary and Mandibular incisor was compared between prediction based on SSA and post treatment outcome. The parameters taken were U1-NA Linear and U1-NA angular and L1-NB Linear and L1-NB angular and comparison were made using Paired Student T test.: No significant difference was seen in U1-NA distance in (mm) and angulation(degree) Compared between predicted values(U1-NA- 2.478mm, 20.53 degree) and post treatment outcome (U1-NA-2.80mm, 22.733 degree) , p>0.05. Similarly no significant difference was seen in L1-NB distance (mm) compared between predicted (L1-NB 3.1227mm) and post treatment outcome(3.487 mm), p>0.05. However significant difference was seen for L1-NB angulation between predicted (22.573) and actual post treatment outcome(26.02), p<0.05.: Steiner Stick Analysis overestimated the values for angulation for Mandibular Incisors. This could be attributed to variability in mechanics or small size sample. Further studies should aim at comparisons done in larger sample size.
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Yusoff, Marina, Muhammad Najib Bin Fathi, and . "Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming." International Journal of Engineering & Technology 7, no. 3.15 (August 13, 2018): 63. http://dx.doi.org/10.14419/ijet.v7i3.15.17408.
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Students’ performance is a key point to get a better first impression during a job interview with an employer. However, there are several factors, which affect students’ performances during their study. One of them is their learning style, which is under Neurolinguistic Programming (NLP) approach. Learning style is divided into a few behavioral categories, Visual, Auditory and Kinesthetics (VAK). This paper addresses the evaluation of clustering methods for the identification of learning style based on system preferences. It starts with the distribution of questionnaires to acquire the information on the VAK for each student. About 167 respondents in the Faculty of Computer and Mathematical Science are collected. It is then pre- processed to prepare the data for clustering method evaluations. Three clustering methods; Simple K-Mean, Hierarchical and Density-Based Spatial Clustering of Applications with Noise are evaluated. The findings show that Simple K-Mean offers the most accurate prediction. Upon completion, by using the dataset, Simple K-Means technique estimated four clusters that yield the highest accuracy of 74.85 % compared to Hierarchical Clustering, which estimated four clusters and Density- Based Spatial Clustering of Applications with Noise which estimated three clusters with 52.69% and 61.68 % respectively. The clustering method demonstrates the capability of categorizing the learning style of students based on three categories; visual, auditory and kinesthetic. This outcome would be beneficial to lecturers or teachers in university and school with an automatically clustering the students’ learning style and would assist them in teaching and learning, respectively.
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Bowman, Thomas G., Jay Hertel, and Heather D. Wathington. "Programmatic Factors Associated with Undergraduate Athletic Training Student Retention and Attrition Decisions." Athletic Training Education Journal 10, no. 1 (January 1, 2015): 5–17. http://dx.doi.org/10.4085/10015.
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Context Athletic training programs (ATPs) are charged with meeting an increased demand for athletic trainers with adequate graduates. Currently, the retention rate of athletic training students in ATPs nationwide and the programmatic factors associated with these retention rates remain unknown. Objective Determine the retention rate for athletic training students nationwide and the programmatic factors associated with retention. Design Cross-sectional online survey. Setting Undergraduate ATPs. Patients or Other Participants Program directors (PDs) of all Commission on Accreditation of Athletic Training Education–accredited undergraduate ATPs were surveyed. We obtained responses from 177 of the 343 PDs (51.6%). Intervention(s) The survey asked PDs for information about their institution, ATP, and themselves. Main Outcome Measure(s) Self-reported retention rate. Results The participants reported an average retention rate of 81.0% ± 17.9%. We found a significant prediction equation (F4,167 = 16.39, R2 = 0.282, P < .001), using the perceptions of student success factor (P < .001, R2 = 0.162), the timing of formal admission (P < .001, R2 = 0.124), the number of years the ATPs had been accredited (P = .001, R2 = 0.039), the number of students admitted to the ATP annually (P = .001, R2 = 0.037), and the number of years the PDs had held their position at their current institution (P = .03, R2 = 0.018). Conclusions Program directors should work to provide a stimulating atmosphere to motivate students. Delaying the formal admission of prospective students may allow athletic training students to make an informed decision to enter an ATP. A rich history of success and consistent leadership can provide an ATP environment that fosters retention. Program directors should carefully consider how many students to admit into the ATP annually, as individual attention may alter retention decisions of athletic training students.
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de Paor, Muireann, Fiona Boland, Xinyan Cai, Susan Smith, Mark H. Ebell, Eoin Mac Donncha, and Tom Fahey. "Derivation and validation of clinical prediction rules for diagnosis of infectious mononucleosis: a prospective cohort study." BMJ Open 13, no. 2 (February 2023): e068877. http://dx.doi.org/10.1136/bmjopen-2022-068877.
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ObjectivesInfectious mononucleosis (IM) is a clinical syndrome that is characterised by lymphadenopathy, fever and sore throat. Although generally not considered a serious illness, IM can lead to significant loss of time from school or work due to profound fatigue, or the development of chronic illness. This study aimed to derive and externally validate clinical prediction rules (CPRs) for IM caused by Epstein-Barr virus (EBV).DesignProspective cohort study.Setting and participants328 participants were recruited prospectively for the derivation cohort, from seven university-affiliated student health centres in Ireland. Participants were young adults (17–39 years old, mean age 20.6 years) with sore throat and one other additional symptom suggestive of IM. The validation cohort was a retrospective cohort of 1498 participants from a student health centre at the University of Georgia, USA.Main outcome measuresRegression analyses were used to develop four CPR models, internally validated in the derivation cohort. External validation was carried out in the geographically separate validation cohort.ResultsIn the derivation cohort, there were 328 participants, of whom 42 (12.8%) had a positive EBV serology test result. Of 1498 participants in the validation cohort, 243 (16.2%) had positive heterophile antibody tests for IM. Four alternative CPR models were developed and compared. There was moderate discrimination and good calibration for all models. The sparsest CPR included presence of enlarged/tender posterior cervical lymph nodes and presence of exudate on the pharynx. This model had moderate discrimination (area under the receiver operating characteristic curve (AUC): 0.70; 95% CI: 0.62–0.79) and good calibration. On external validation, this model demonstrated reasonable discrimination (AUC: 0.69; 95% CI: 0.67–0.72) and good calibration.ConclusionsThe alternative CPRs proposed can provide quantitative probability estimates of IM. Used in conjunction with serological testing for atypical lymphocytosis and immunoglobulin testing for viral capsid antigen, CPRs can enhance diagnostic decision-making for IM in community settings.
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Ramaswami, Gomathy, Teo Susnjak, Anuradha Mathrani, James Lim, and Pablo Garcia. "Using educational data mining techniques to increase the prediction accuracy of student academic performance." Information and Learning Sciences 120, no. 7/8 (July 8, 2019): 451–67. http://dx.doi.org/10.1108/ils-03-2019-0017.
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Purpose This paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation. Design/methodology/approach Classification data mining techniques have been adapted to predict students’ academic performance. Four algorithms, Naïve Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions. Findings The results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students’ final-year academic performance for the subsequent year. Research limitations/implications The present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of students’ performance. Future work will address some of these limitations. Originality/value The model developed from this research can provide value to institutions in making process- and data-driven predictions on students’ academic performances.
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DeRuisseau, Lara R. "The flipped classroom allows for more class time devoted to critical thinking." Advances in Physiology Education 40, no. 4 (December 1, 2016): 522–28. http://dx.doi.org/10.1152/advan.00033.2016.
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The flipped classroom was utilized in a two-semester, high-content science course that enrolled between 50 and 80 students at a small liberal arts college. With the flipped model, students watched ~20-min lectures 2 days/wk outside of class. These videos were recorded via screen capture and included a detailed note outline, PowerPoint slides, and review questions. The traditional format included the same materials, except that lectures were delivered in class each week and spanned the entire period. During the flipped course, the instructor reviewed common misconceptions and asked questions requiring higher-order thinking, and five graded case studies were performed each semester. To determine whether assessments included additional higher-order thinking skills in the flipped vs. traditional model, questions across course formats were compared via Blooms Taxonomy. Application-level questions that required prediction of an outcome in a new scenario comprised 38 ± 3 vs. 12 ± 1% of summative assessment questions (<0.01): flipped vs. traditional. Final letter grades in both formats of the course were compared with major GPA. Students in the flipped model performed better than their GPA predicted, as 85.5% earned a higher grade (vs. 42.2% in the traditional classroom) compared with their major GPA. These data demonstrate that assessments transitioned to more application-level compared with factual knowledge-based questions with this particular flipped model, and students performed better in their final letter grade compared with the traditional lecture format. Although the benefits to a flipped classroom are highlighted, student evaluations did suffer. More detailed studies comparing the traditional and flipped formats are warranted.