Relevant bibliographies by topics / Student Outcome Prediction

Academic literature on the topic 'Student Outcome Prediction'

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Published: 14 March 2023

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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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Dissertations / Theses on the topic "Student Outcome Prediction"

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Sandusky, Sue Ann. "Predicting Student Veteran Persistence." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1585070424571773.

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Predy, Larissa Kristine. "Predicting student outcomes using office referral data from a national sample of middle school students." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43817.

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This study examined the adequacy of using Office Discipline Referral (ODR) data to predict student behaviour outcomes. Specifically, the study identified rates of ODRs and student trajectories in a middle school population and assessed whether end-of-year behaviour outcomes can reliably be predicted as early as the first few months of Grades 6, 7, and 8. Participants were 401,852 students from 593 public middle schools (serving Grades 6 to 8) in the United States whose ODR data had been entered in the School-wide Information System (SWIS, 2012) during the 2009-2010 school year. ODRs were categorized by final ODR cut points used in positive behaviour support systems (0-1, 2-5, and 6+ total ODRs per year). Descriptive analyses and multilevel multinomial logistic regression analyses were performed; Receiver Operating Characteristic (ROC) analyses were used to assess diagnostic accuracy. Results showed relatively stable mean increases in ODRs throughout the school year for students in each final ODR category, though median growth trajectories revealed a comparatively large increase in ODRs received in November and February. Results also showed that ODRs received in September, October, and November were statistically significant predictors of final behaviour outcomes, and the inclusion of types of referrals (especially for defiance) significantly improved prediction of the final ODR category. These results are discussed with regards to previous and future research, limitations, and the utility of ODRs for intervention decision-making in middle schools.
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Bleecker, Wendy S. "Predicting student outcomes for Washington State middle schools using school counselor's and administrator's racial consciousness and organizational variables." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Dissertations/Fall2007/w_bleecker_113007.pdf.

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Johnston, Jaures Prescott. "Predicting Educational Outcomes For Students Returning From Incarceration." Diss., Temple University Libraries, 2009. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/42850.

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School Psychology
Ph.D.
During the 2005-2006 school year, 967 students returned from incarceration and were assigned to RETI-WRAP (Re-Entry Transition Initiative-Welcome Return Assessment Process), a ten-day transition program operated by the School District of Philadelphia designed to review, evaluate, and make recommendations for appropriate school placement upon their return to the public school system. The current study employed a retrospective analysis of archival data from the ’05-’06 school year in order to identify those variables that predict successful transition (active in school or graduated). The data included demographic information (e.g., gender, grade, high school credits, and race), educational placement (e.g., regular or special education), severity of crime and reading and math scores as determined by standardized testing conducted by RETI-WRAP personnel. Eight variables were used to determine the prevalence, relationships, and predictive power of demographic, academic, and crime-related variables. Frequency distributions, Pearson correlations, Phi coefficients, and discriminant function analysis were conducted to examine prevalence, associations between variables, and predictions to successful re-entry. A significant Wilks’ Lamba of .945 was obtained for the sole discriminant function. Three variables emerged as significant predictors of successful re-entry: the number of credits obtained, the severity of the crime committed, and the age of the student. Younger students with more credits who committed less severe crimes were more likely to have achieved a successful transition. The amount of variance (5%) explained by the statistical model was limited by the imbalanced nature of the sample, in that few students (21.9%) experienced a successful transition. The current study highlighted the dynamics and overall profile of one of the most challenging and vulnerable populations in the public school system. By using database decision- making and providing a comprehensive framework to understand the characteristics of students who transition successfully, policy makers are in a better position to identify an optimal placement match based on empirical findings, thus decreasing the number of students who drop out of school or who remain involved with the juvenile justice system.
Temple University--Theses
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Allen, Patricia Hayden. "The relationship of learner entry characteristics and reading and writing skills to program exit outcome." FIU Digital Commons, 1994. http://digitalcommons.fiu.edu/etd/1141.

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An approach to enhancing the success of nursing students is found in understanding the learning process and in the academic and sociologic variables placing students at risk for failure and attrition. Utilizing Bloom's Mastery Model, nurse educators may reduce failure and attrition by enhancing alterable variables. This Ex Post Facto investigation utilized Bloom's learning theory to examine a causal relationship of learner entry characteristics, learner reading and writing skills and the impact on program exit grade point average. The study sample was comprised of 143 nursing students entering an upper division urban multicultural baccalaureate nursing program. Data were collected by use of a demographic questionnaire, assessment of reading and writing skills of junior students in the nursing program, and obtainment of the program exit grade point average. A recursive path analysis was utilized for data analysis. Findings revealed older male students who transferred to the program from a university with high entry grade point averages excelled in reading assessment scores. University transfer students with a high entry grade point average excelled in writing also. Students for who French, specifically Creole, was a first language had lower writing scores and program exit grade point averages. Spanish as a first language was also associated with lower exit grade point averages. Higher reading and writing scores and entry grade point averages were associated with higher program exit grade point averages. Finally entry grade point average and university transfer were the only entry characteristics mediated by both reading and writing scores.
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Wood, Robert G. "Predicting the outcome of leadership identification from a college student's experiences." W&M ScholarWorks, 2005. https://scholarworks.wm.edu/etd/1550154193.

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Wang, Xueli. "From Access to Success: Factors Predicting the Educational Outcomes of Baccalaureate Aspirants Beginning at Community Colleges." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1215015456.

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Wood, Julie E. "Predicting School Success From A Disruption in Educational Experience." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1477645391441543.

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Faas, Caitlin Suzanne. "Predicting Socioeconomic Success and Mental Health Outcomes for Young Adults who Dropped out of College." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23934.

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This dissertation is comprised of two studies that both aimed to understand the population of young adults who dropped out of college. Life course theory and the theory of emerging adulthood were used to create the theoretical foundation for the studies. The first study explored how students who dropped out of college were faring during young adulthood on multiple measures of well-being (personal income, job satisfaction, subjective socioeconomic success, mastery, happiness, depression, and stress). Five latent classes emerged from the data, which demonstrated the heterogeneity within the sample (N = 1,530). The second study then utilized the same sample to examine how transitions into adulthood predicted well-being during young adulthood while controlling for family of origin resources and developmental assets. The transitions to adulthood included timing of marriage, parenthood, and whether or not the young adult was living independently of their parents or not. The hypotheses based on theory were partially supported, with some differences existing between men and women. The discussion reviews the implications for practice and policy.
Ph. D.
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Hardesty, Robin B. "Stress, Coping, and their Prediction of Mental Health Outcomes in International Baccalaureate High School Students." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/3869.

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This study investigated the mental health of high school students enrolled in the International Baccalaureate (IB) High School Diploma Program (n =139) in a large, southeastern high school. Mental health was assessed using both positive indicators (life satisfaction, academic achievement, academic self-efficacy) and negative indicators (psychopathology) indicators of adolescent social-emotional and school functioning. Findings from this study include that IB students perceive more stress than their general education peers, yet maintain mental health that is equivalent or superior to that of their general education peers. The role of coping in predicting mental health outcomes in IB students was also investigated. Findings indicate that specific coping styles are differentially related to mental health outcomes in this sub-population of adolescents. Furthermore, coping styles moderate the influence of stress on global life satisfaction and internalizing psychopathology. These findings suggest that participation in the academically rigorous and time-intensive IB program is not harmful to the mental health of high school students, and in fact may be beneficial, as evidenced by the superior academic functioning of students in the IB program.
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Books on the topic "Student Outcome Prediction"

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Resource allocation and student achievement: A microlevel impact study of differential resource inputs on student achievement outcomes. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1996.

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Schmitt, Neal. Combining Cognitive and Noncognitive Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199373222.003.0012.

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The use of noncognitive measures enhances the prediction of various individual outcomes. Although structured measures of noncognitive constructs are routinely used in employee selection, they are rarely used to predict college student success. Situational judgment, biodata measures, and other methods of measurement address constructs that add to the prediction of grade point average and are major correlates of other student outcomes. Employers and college administrators indicate that outcomes other than task performance and grades are important. Because noncognitive attributes are the best predictors of these alternative outcomes, it seems they should be included in the set of criteria used by college admissions personnel. Several issues should be addressed if noncognitive measures are used more frequently, including the possibility of their use in other ways than for selection only, the minimizing of the influence of faking, and the reaction of various constituencies to their use in college admissions decisions.
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Book chapters on the topic "Student Outcome Prediction"

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Wang, Tianqi, Fenglong Ma, Tang Tang, Longfei Zhang, and Jing Gao. "Textbook Enhanced Student Learning Outcome Prediction." In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), 352–60. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2022. http://dx.doi.org/10.1137/1.9781611977172.40.

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Wang, Tianqi, Fenglong Ma, Yaqing Wang, Tang Tang, Longfei Zhang, and Jing Gao. "Towards Learning Outcome Prediction via Modeling Question Explanations and Student Responses." In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), 693–701. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976700.78.

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Kubayi, Shiluva Claudia, Ashwini Jadhav, and Ritesh Ajoodha. "A Machine Learning Approach for Predicting Students’ Second-Year Outcomes." In Algorithms for Intelligent Systems, 535–47. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3951-8_41.

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Zaporozhko, Veronika V., Denis I. Parfenov, and Vladimir M. Shardakov. "Development Approach of Formation of Individual Educational Trajectories Based on Neural Network Prediction of Student Learning Outcomes." In Advances in Intelligent Systems and Computing, 305–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39162-1_28.

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De Witte, Kristof, and Marc-André Chénier. "Learning Analytics in Education for the Twenty-First Century." In Handbook of Computational Social Science for Policy, 305–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16624-2_16.

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AbstractThe online traces that students leave on electronic learning platforms; the improved integration of educational, administrative and online data sources; and the increasing accessibility of hands-on software allow the domain of learning analytics to flourish. Learning analytics, as in interdisciplinary domain borrowing from statistics, computer sciences and education, exploits the increased accessibility of technology to foster an optimal learning environment that is both transparent and cost-effective. This chapter illustrates the potential of learning analytics to stimulate learning outcomes and to contribute to educational quality management. Moreover, it discusses the increasing emergence of large and accessible data sets in education and compares the cost-effectiveness of learning analytics to that of costly and unreliable retrospective studies and surveys. The chapter showcases the potential of methods that permit savvy users to make insightful predictions about student types, performance and the potential of reforms. The chapter concludes with recommendations, challenges to the implementation and growth of learning analytics.
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Kavitha, R. K., N. Jayakanthan, and S. Harishma. "Predicting Students’ Outcomes with Respect to Trust, Perception, and Usefulness of Their Instructors in Academic Help Seeking Using Fuzzy Logic Approach." In Advancements in Smart Computing and Information Security, 233–43. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23092-9_19.

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Järvinen, Tero, Jenni Tikkanen, and Piia af Ursin. "The Significance of Socioeconomic Background for the Educational Dispositions and Aspirations of Finnish School Leavers." In Finland’s Famous Education System, 243–56. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8241-5_15.

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AbstractThis chapter examines the significance of socioeconomic background (SES) for the educational dispositions and aspirations of Finnish comprehensive school leavers. After demonstrating the existence of the relationship between the students’ SES background and their dispositions and aspirations, the main question addressed is whether this relationship changes when controlling for the effect of students’ academic ability as measured by their literacy skills. In our examination, we draw on a study of 15-year-old lower secondary school students (n = 1058) in Turku sub-region consisting of the city of Turku and ten smaller, surrounding municipalities. The results of our study are mixed. Students with high-level literacy skills have positive dispositions towards learning and education despite their socioeconomic background. The same is, however, not the case with educational aspirations. Among low-SES students, individual ability does not predict high educational aspirations in a similar manner that it does among high-SES students. This finding poses a challenge for the Finnish education system. If SES is a more significant predictor of educational aspirations of an individual than ability or motivation, there are negative effects for both individuals themselves and society. From the individual point of view, self-exclusion of gifted low-SES students from higher education decreases their future labour market opportunities and outcomes. From the societal point of view, in turn, it means that many occupational fields will lose potentially talented and skilful employees. In these respects, the Finnish education system would not only be unequal but also inefficient.
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Dianah, Siti, Ali Selamat, and Ondrej Krejcar. "Improve Imbalanced Multiclass Classification Based on Modified SMOTE and Feature Selection for Student Grade Prediction." In Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning, 371–89. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8686-0.ch014.

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In higher education institutions (HEI), the ability to predict student grades as an early warning system is one of the important areas that gained attention to improve educational outcomes. Over the years, machine learning techniques have facilitated and successfully addressed student grade prediction for identifying the potentially weak students in a particular course. However, dealing with an imbalanced multiclass classification dataset is challenging due to biased results towards predicting the minority class. Therefore, this chapter proposes a method that can increase the classification performance by using a modified synthetic minority oversampling technique and feature selection (MSMOTE-FS). The experiments tested the proposed method's effectiveness by utilizing four oversampling techniques and six standard classification algorithms. This finding indicated that the proposed method gives promising results to improve the accuracy in multiclass classification of student grade prediction.
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Aslam, M. M. Haris, Ahmed F. Siddiqi, Khuram Shahzad, and Sami Ullah Bajwa. "Predicting Student Academic Performance." In Business Intelligence, 1445–62. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9562-7.ch070.

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The biggest challenge in nurturing an academic community is encouraging knowledge sharing among its members. Literature on communities, however, has paid less attention on the role of outcome expectations in encouraging the knowledge sharing behaviors. This study examines the effects of Personal Outcome Expectations (POE) and Community-related Outcome Expectations (COE) on the knowledge sharing behaviors of students and its consequent impact on their academic performance. In order to study these relationships a survey of university students was conducted. Based on structural equation modeling approach, it was found that COE have significant impact on knowledge sharing among the students.
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Alcolea, Juan J., Alvaro Ortigosa, Rosa M. Carro, and Oscar J. Blanco. "Best Practices in Dropout Prediction." In Early Warning Systems and Targeted Interventions for Student Success in Online Courses, 301–23. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-5074-8.ch015.

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This chapter focuses on the key practical aspects to be considered when facing the task of developing predictive models for student learning outcomes. It is based on the authors' experience building and delivering dropout prediction models within higher education contexts. The chapter presents the information used to generate the predictive models, how this information is treated, how the models are fed, which types of algorithms have been used, and why and how the obtained results have been evaluated. It recommends best practices for building, training, and evaluating predictive models. It is hoped that readers will find these recommendations useful for the design, development, deployment, and use of early warning systems.
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Conference papers on the topic "Student Outcome Prediction"

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Felix, Igor, Ana Paula Ambrósio, PRISCILA DA SILVA LIMA, and Jacques Duílio Brancher. "Data Mining for Student Outcome Prediction on Moodle: a systematic mapping." In XXIX Simpósio Brasileiro de Informática na Educação (Brazilian Symposium on Computers in Education). Brazilian Computer Society (Sociedade Brasileira de Computação - SBC), 2018. http://dx.doi.org/10.5753/cbie.sbie.2018.1393.

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Sahu, Devesh, Rishi Sharma, Devesh Bharti, and Utkarsh Narain Srivastava. "Control Algorithm for Anti-Lock Braking System." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64640.

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Safer, controlled and efficient braking is the primary objective of Anti-lock Braking System wherein an efficient and robust braking system significantly enhances the vehicle performance during both straight line motion and cornering thus resulting in drastic reduction of stopping time and distance especially for a race car in long run. Hence clocking better lap times and a considerable reduction in wear of tires are an obvious outcome apart from the enhanced vehicle stability. This work on Anti-lock Braking System (ABS) prediction and control algorithm deals with technical paradigm for estimation of vehicle velocity using wheel angular velocity from wheel rpm sensors as the sole input and methodology to control the braking torque on each wheel so as to prevent loss of traction. The proposed algorithm is modeled using advanced simulating tools involving theoretical estimation of braking torque on each wheel. This is supposed to reduce tire skid with controlled wheel slip estimated using the tire data and the car vehicle dynamics with formula student vehicle as the subject. The work and hence the control algorithm can potentially be extended into a better traction control strategy with acceleration and yaw inputs from accelerometers and yaw sensors.
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Hu, Han, and Connor Heo. "Integration of Data Science Into Thermal-Fluids Engineering Education." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-88193.

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Abstract To improve the integration of data science into thermal fluids education, a technical elective course is developed to introduce a wide range of machine learning and deep learning algorithms to engineering students, including principal component analysis, multiplayer perceptron, convolutional neural networks (CNN), long short-term memory (LSTM) networks, reinforcement learning (RL), generative algorithms (GA), and generative adversarial networks for mechanical engineering applications, including visualization-based physical quantity predictions, dynamic signal classification, and prediction, data-driven control of dynamical systems, surrogate modeling, dimensionality reduction, among others. The lectures cover the fundamental concepts and examples of developing machine learning models using Python and MATLAB. To facilitate students’ practice of applying data science in solving mechanical engineering progress, this course has touchpoints in several key areas of mechanical engineering, including fluid mechanics, heat transfer, materials science, design, and dynamics/control. Twenty-five students, including seven undergraduate and eighteen graduate students, took the course and the outcomes are very fruitful and encouraging. A variety of data science algorithms have been leveraged to solve mechanical engineering research problems, including generative designs of air-cooled heat sinks, Gaussian process regression for battery lifetime prediction and femtosecond laser manufacturing parameters, GA for two-phase cooling heat exchanger design, coupled PCA and LSTM for microcapsule deformation prediction, CNN for boiling regime classification and laser-manufactured textures classification, bidirectional recurrent neural networks basecalling of DNA and RNA sequences, coupled PCA and MLP porous medium morphology classification, RL for the control of soft robotics. The course has so far led to 25 student-centered machine learning projects, two conference papers, and an Honors thesis.
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McKillop, Conor. "Predicting the Outcome of Deliberative Democracy: A Research Proposal." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-2013.

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Kotova, Elena E., and Andrei S. Pisarev. "Adaptive prediction of student learning outcomes in online mode." In 2017 IEEE II International Conference on Control in Technical Systems (CTS). IEEE, 2017. http://dx.doi.org/10.1109/ctsys.2017.8109509.

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Simjanoska, Monika, Marjan Gusev, Sasko Ristov, and Ana Madevska Bogdanova. "Intelligent student profiling for predicting e-Assessment outcomes." In 2014 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2014. http://dx.doi.org/10.1109/educon.2014.6826157.

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Eveloy, Valerie, Shrinivas Bojanampati, and Peter Rodgers. "Teaching of Beam Deflection Analysis Through Laboratory Experiments." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65195.

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Hands-on laboratory skills play a vital role in providing students with a sound understanding of the scientific fundamentals and their application in solving real-life engineering problems. This paper describes a hands-on laboratory project focused on solid mechanics, which is taught as part of a one-semester, junior-level mechanical engineering course titled Core Measurements Laboratory. The project requires students to design, construct and commission an experimental test facility for the characterization of cantilever beam deflection and stress due to pure bending using concomitant methods, consisting of three experimental techniques and analytical predictions. Beam deflection is measured using both a linear displacement transducer (LDT) and digital camera, with corresponding stress measurements derived from strain gage measurements. These measurements are compared with analytical predictions. The project is undertaken over a five-week period by a team of four students. A written project report and oral presentation are conducted on project completion. Emphasis is placed on elaboration of a test plan, sensor installation, data acquisition and LabVIEW programming, as well as analysis of discrepancies between measurements and predictions. The teaching strategy employed to integrate fundamental theory with hands-on experiences is described, with a sample of the measurement results presented. The effectiveness of the laboratory project in enhancing student fundamental and applied knowledge in the subject area, and project management skills, is assessed by monitoring student performance improvements over the duration of the project, as well as through student surveys. On a scale of 1 (deficient) to 4 (exemplary), overall class scores of 2.92, 3.25 and 3.4 were obtained for ABET Criterion 3 Outcomes (b), (d) and (g), respectively, which were assessed using the hands-on project final report and oral presentation. The student survey indicates that student knowledge in fundamentals, sensors, data acquisition and LabVIEW programming improved by 16%, 23%, 30% and 48%, respectively, with the hands on project.
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Schoeffel, Pablo, Vinicius Faria Culmant Ramos, and Raul Sidnei Wazlawick. "A Method to Predict At-risk Students in Introductory Computing Courses Based on Motivation." In Workshops do Congresso Brasileiro de Informática na Educação. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/cbie.wcbie.2020.41.

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Despite being a problem reported in a long time, the high rate of dropout and failure in computing courses remains a problem. Although there is a strong relationship between the motivation and the students outcome, few works use the motivation as a factor to identify students at risk. This work presents and evaluates a method to identify features that allow predicting at-risk students in introductory computing courses, based on four main components: pre-university factors, initial motivation, motivation through the course, and professor perception. The method created, named EMMECS, was applied with 245 students from different programs in four different universities in southern Brazil. We carried out several simulations of prediction, using ten different classification algorithms and different datasets. As a result, using support vector machine and AdaBoostM1 algorithms, we identified on average more than 80% of students that would fail, since the first week of the study. The results show that the proposed method is effective compared with related works and it has as advantages its independence of programmatic content, specific assessments, grades, and interaction with learning systems. Furthermore, the method allows the weekly prediction, with good results since the first few weeks.
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Nolte, Hannah, Catherine Berdanier, Jessica Menold, and Christopher McComb. "Comparison of Exams and Design Practica for Assessment in First Year Engineering Design Courses." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22054.

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Abstract In response to calls for engineering programs to better prepare students for future careers, many institutions offer courses with a design component to first-year engineering students. This work proposes that traditional exam-based assessments of design concepts are inadequate, and alternative forms of assessment are needed to assess student learning in design courses. This paper investigates the self-efficacy differences between a traditional exam and a two-part practicum as a mid-semester assessment for introductory engineering students enrolled in a first-year design course. Increased self-efficacy has been linked to various positive student outcomes and increased retention of underrepresented students. The practicum consisted of an in-class team design task and an out-of-class individual reflection, while the exam was a traditional, individual written exam. All students completed a pre-assessment survey and a post-assessment survey, both of which included measures of design self-efficacy. Analysis showed that the practicum increased the design self-efficacy of students more effectively than the exam. Students who identified as women had greater gains in design self-efficacy during the practicum as compared to men. Identifying as a minority subgroup student was also trending towards being a significant predictor of change in self-efficacy for the practicum. Findings suggest that a mid-semester practicum is a successful assessment of design competencies that contributes to increased first-year engineering student self-efficacy.
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Padilha, TPP, and R. Catrambone. "USE OF THE TENSORFLOW FRAMEWORK TO SUPPORT EDUCATIONAL PROBLEMS: A SYSTEMATIC MAPPING." In The 7th International Conference on Education 2021. The International Institute of Knowledge Management, 2021. http://dx.doi.org/10.17501/24246700.2021.7133.

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The Google framework called TensorFlow has been widely used for decision making in several areas, including Education. Predicting student risk and optimizing a student’s learning path are, for example, two traditional educational problems that have been explored for years but there are a myriad of different data mining approaches involved. This paper’s goal is to illustrate the results of a systematic mapping process conducted on educational data mining studies using the TensorFlow framework. Furthermore, this paper will assist in illustrating what kind of problems to focus on (which can paradoxically be seen as opportunities), identify, demonstrate, and catalogue all the academic studies that have discussed it, and the approaches leveraged (neural network, decision tree, natural language processing, and so on). The mapping process followed five phases with rigor, returning a set of 32 relevant papers in the study area with detailed information related to the research questions. The outcome of this systematic study will be of benefit to academic managers, researchers, and students who use this framework as support to solve educational problems. Keywords: systematic mapping; Educational problems; TensorFlow; data mining
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Reports on the topic "Student Outcome Prediction"

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Sowjanya, Dr Kaniti, Dr Bongu Srinivas, and Dr Metta Lakshmana Rao. A STUDY ON FIBROSCAN COMPARED TO AST TO PLATELET RATIO INDEX(APRI) FOR ASSESSMENT OF LIVER FIBROSIS WITH NONALCOHOLIC FATTY LIVER DISEASE(NAFLD). World Wide Journals, February 2023. http://dx.doi.org/10.36106/ijar/1606016.

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Background : Nonalcoholic fatty liver disease (NAFLD) is increasingly recognized as a cause of chronic liver disease, and often results in the devastating outcomes of decompensated liver cirrhosis and hepatocellular carcinoma and is an important feature of metabolic syndromes and insulin resistance. The assessment of liver brosis is essential for predicting the prognosis and outcome of all forms of chronic liver disease. A liver biopsy is the gold standard for the assessment of liver brosis, but it has its limitations, which include life-threatening complications. Alternative methods of non-invasive laboratory and radiological testing for the assessment of liver brosis in NAFLD have evolved during the past decade, and these methods may be able to overcome the limitations of liver biopsy. These methods include the AST/ALT ratio, the AST platelet ratio index (APRI), and the Fibrosis 4 (FIB-4) score. This study was conducted in order to assess liver brosis using Fibroscan, and to compare these results to the use of AST platelet ratio index (APRI scores), and the AST/ALT ratios on NAFLD patients. METHODS: This was a cross sectional study conducted in King George Hospital Visakhapatnam,A total 122 patients were studied of which 65 were males and 57 were females.all the patients were subjected to relevant investigations including ultrasound abdomen,serum liver enzymes,broscan. The (SPSS) version 20 was used for the analysis.A Student's t-test was used to compare the AST/ALT ratio to the APRI scores between patients with advanced brosis higher than F2 and patients with mild to moderate brosis of F2 or less. RESULTS :The data showed that a high percentage of the NAFLD patients exhibited advanced stages of liver brosis based on the Fibroscan examinations. These results were supported by the strong correlation between the Fibroscan results and the AST/ALT ratio and APRI scores. Correlation analysis showed a signicant positive correlation between age and brosis scores (r = 0.27 with P = 0.004 for Pearson correlations). On the other hand, a signicant negative correlation between platelet count and stiffness scores was obtained (r = - 0.315 with P= 0.001 for Pearson correlations). Serum ALT level was determined to be signicantly negatively correlated with age by using Spearman correlations (r = - 0.232, and P = 0.022). A signicant positive correlation was observed between serum ALT and hepatic stiffness measurements using Spearman correlations (r = 0.284, and P = 0.005). This study has shown that the combination of Fibroscan CONCLUSION: and AST/ALT and APRI methods provides a valuable approach for assessing liver brosis in NAFLD patients. This can eliminate the need for liver biopsy in patients without clear indication
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Sandford, Robert, Vladimir Smakhtin, Colin Mayfield, Hamid Mehmood, John Pomeroy, Chris Debeer, Phani Adapa, et al. Canada in the Global Water World: Analysis of Capabilities. United Nations University Institute for Water, Environment and Health, November 2018. http://dx.doi.org/10.53328/vsgg2030.

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This report critically examines, for the first time, the capacity of Canada’s water sector with respect to meeting and helping other countries meet the water-related targets of the UN’s global sustainable development agenda. Several components of this capacity are examined, including water education and research, investment in water projects that Canada makes internally and externally, and experiences in water technology and governance. Analysis of the water education system suggests that there is a broad capability in institutions of higher learning in Canada to offer training in the diverse subject areas important in water. In most cases, however, this has not led to the establishment of specific water study programmes. Only a few universities provide integrated water education. There is a need for a comprehensive listing of water-related educational activities in universities and colleges — a useful resource for potential students and employers. A review of recent Canadian water research directions and highlights reveals strong and diverse water research capacity and placed the country among global leaders in this field. Canada appears to be within the top 10 countries in terms of water research productivity (publications) and research impact (citations). Research capacity has been traditionally strong in the restoration and protection of the lakes, prediction of changes in climate, water and cryosphere (areas where water is in solid forms such as ice and snow), prediction and management of floods and droughts. There is also a range of other strong water research directions. Canada is not among the top 10 global water aid donors in absolute dollar numbers; the forerunners are, as a rule, the countries with higher GDP per capita. Canadian investments in Africa water development were consistently higher over the years than investments in other regions of the global South. The contributions dropped significantly in recent years overall, also with a decline in aid flow to Africa. Given government support for the right business model and access to resources, there is significant capacity within the Canadian water sector to deliver water technology projects with effective sustainable outcomes for the developing world. The report recommends several potential avenues to elevate Canada’s role on the global water stage, i.e. innovative, diverse and specific approaches such as developing a national inventory of available water professional capacity, and ranking Universities on the strength of their water programmes coordinating national contributions to global sustainability processes around the largest ever university-led water research programme in the world – the 7-year Global Water Futures program targeting specific developmental or regional challenges through overseas development aid to achieve quick wins that may require only modest investments resolving such chronic internal water challenges as water supply and sanitation of First Nations, and illustrating how this can be achieved within a limited period with good will strengthening and expanding links with UN-Water and other UN organisations involved in global water policy work To improve water management at home, and to promote water Canadian competence abroad, the diverse efforts of the country’s water sector need better coordination. There is a significant role for government at all levels, but especially federally, in this process.
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