Relevant bibliographies by topics / Data processing in higher education

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data processing in higher education'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Data processing in higher education"

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Zörög, Zoltán, Tamás Csomós, and Csaba Szűcs. "ERP systems in higher education." Applied Studies in Agribusiness and Commerce 6, no. 3-4 (November 30, 2012): 103–9. http://dx.doi.org/10.19041/apstract/2012/3-4/14.

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In the past few decades data processing and in-company communication has changed significantly. First there were only a few computers purchased at companies, therefore departments developed applications that covered corporate administration which lead to so called isolated solutions. These days with the spread of electronic data processing the greatest problem for companies is not gaining information – since they can be found in all sorts of databases and data warehouses as internal or external information – rather producing information that is necessary in a given situation. What can help to solve this situation? It is informatics, more precisely ERP systems which have substituted software that provided isolated solutions at companies for decades. System based thinking is important in their application beside the fact that only data absolutely necessary for managerial decisions must be produced. This paper points out why we consider practice oriented teaching of ERP systems in higher education important.
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García-Vélez, Roberto Agustín, Martín López-Nores, Gabriel González-Fernández, Vladimir Espartaco Robles-Bykbaev, Manolis Wallace, José J. Pazos-Arias, and Alberto Gil-Solla. "On Data Protection Regulations, Big Data and Sledgehammers in Higher Education." Applied Sciences 9, no. 15 (July 31, 2019): 3084. http://dx.doi.org/10.3390/app9153084.

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Universities in Latin America commonly gather much more information about their students than allowed by data protection regulations in other parts of the world. We have tackled the question of whether abundant socio-economic data can be harnessed for the purpose of predicting academic outcomes and, thereby, taking proactive actions in student attention, course planning and resource management. A study was conducted to analyze the data gathered by a private university in Ecuador over more than 20 years, to normalize them and to parameterize a Multi-Layer Perceptron neural network, whose best-performing configuration would be used as a benchmark for the comparison of more recent and sophisticated Artificial Intelligence techniques. However, an extensive scan of hyperparameters for the perceptron—exploring more than 12,000 configurations—revealed no significant relationships between the input variables and the chosen metrics, suggesting that there is no gain from processing the extensive socio-economic data. This finding contradicts the expectations raised by previous works in the related literature and in some cases highlights important methodological flaws.
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Pratsri, Sajeewan, and Prachyanun Nilsook. "Design on Big data Platform-based in Higher Education Institute." Higher Education Studies 10, no. 4 (October 8, 2020): 36. http://dx.doi.org/10.5539/hes.v10n4p36.

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According to a continuously increasing amount of information in all aspects whether the sources are retrieved from an internal or external organization, a platform should be provided for the automation of whole processes in the collection, storage, and processing of Big Data. The tool for creating Big Data is a Big Data challenge. Furthermore, the security and privacy of Big Data and Big Data analysis in organizations, government agencies, and educational institutions also have an impact on the aspect of designing a Big Data platform for higher education institute (HEi). It is a digital learning platform that is an online instruction and the use of digital media for educational reform including a module provides information on functions of various modules between computers and humans. 1) Big Data architecture is a framework for an architecture of numerous data which consisting of Big Data Infrastructure (BDI), Data Storage (Cloud-based), processing of a computer system that uses all parts of computer resources for optimal efficiency (High-Performance Computing: HPC), a network system to detect the target device network. Thereafter, according to Hadoop’s tools and techniques, when Big Data was introduced with Hadoop's tools and techniques, the benefits of the Big Data platform would provide desired data analysis by retrieving existing information, to illustrate, student information and teaching information that is large amounts of information to adopt for accurate forecasting.
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Amare, Meseret Yihun, and Stanislava Simonova. "Learning analytics for higher education: proposal of big data ingestion architecture." SHS Web of Conferences 92 (2021): 02002. http://dx.doi.org/10.1051/shsconf/20219202002.

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Research background: Higher education institutions are generating multiple formats of data from diverse sources across the globe. The data ingestion layer is responsible for collecting data and transform for analysis. Learning analytics plays a vital role in providing decision-making support and selection of suitable timely intervention. The lack of tailored big-data ingestion architectures for academics led to several implementation challenges. Purpose of the article: The purpose of this article is to propose data ingestion architecture enabled for big data learning analytics. Methods: The study reviews existing literature to examine big-data ingestion tools and frameworks; and identify big-data ingestion challenges. An optimized framework for the real world learning analytics application was not yet in place at global higher educations. Consequently, the big-data ingestion pipeline is experiencing challenges of inefficient and complex data access, slow processing time, and security issues associated with transferring data to the system. The proposed data ingestion architecture is based on review of recent literature and adapts best international practices, guidelines, and techniques to meet the demand of current big-data ingestion issues. Findings & value added: This study identifies the current global challenges in implementing learning analytics projects. Review of recent big data ingestion techniques has been done based on defined metrics tuned for learning analytics purposes. The proposed data ingestion framework would increase the effectiveness of collecting, importing, processing and storing of learning data. Besides, the proposed architecture contributes to the construction of full-fledged big-data learning analytics ecosystem of higher educations.
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Zhang, Guolei, Jia Li, and Li Hao. "Cloud Computing and Its Application in Big Data Processing of Distance Higher Education." International Journal of Emerging Technologies in Learning (iJET) 10, no. 8 (December 14, 2015): 55. http://dx.doi.org/10.3991/ijet.v10i8.5280.

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In the development of information technology the development of scientific theory has brought the progress of science and technology. The progress of science and technology has an impact on the educational field, which changes the way of education. The arrival of the era of big data for the promotion and dissemination of educational resources has played an important role, it makes more and more people benefit. Modern distance education relies on the background of big data and cloud computing, which is composed of a series of tools to support a variety of teaching mode. Clustering algorithm can provide an effective evaluation method for students' personality characteristics and learning status in distance education. However, the traditional K-means clustering algorithm has the characteristics of randomness, uncertainty, high time complexity, and it does not meet the requirements of large data processing. In this paper, we study the parallel K-means clustering algorithm based on cloud computing platform Hadoop, and give the design and strategy of the algorithm. Then, we carry out experiments on several different sizes of data sets, and compare the performance of the proposed method with the general clustering method. Experimental results show that the proposed algorithm which is accelerated has good speed up and low cost. It is suitable for the analysis and mining of large data in the distance higher education.
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Prykhodko, Kseniia, Olena Khil, Olena Pobirchenko, Oksana Umrixina, Vera Kalabska, and Olha Bobyr. "Art education on big data and digital platforms base of higher education institutions." International journal of health sciences 6, no. 1 (February 21, 2022): 357–65. http://dx.doi.org/10.53730/ijhs.v6n1.4440.

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The study aimed to identify Big Data components and the role of the digital platform used for art education. In addition, the aim can be considered a definition of participants' role in the educational process in a broad application of Big Data and digital platforms. In conducting the research, a comprehensive approach was actively used, as well as descriptive methods, qualitative and quantitative ways of monitoring. We used questionnaires to get the necessary information, studied the essential literary sources, collected and analyzed data, and summarized the results. After summarizing the information obtained, it became clear that the Big Data use and digital platforms for art education allowed the transfer of the classical educational process to digital media and created the necessary environment for intellectualizing the educational process. In the future, it is worth considering and searching for options on how to get rid of the gaps in the use of information technology for art education. Relevant issues can be viewed as the problems of security, confidentiality, ethical component, compatibility, data storage, and processing problems, and the acute experienced personnel shortage.
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Matto, George. "Big Data Analytics Framework for Effective Higher Education Institutions." Tanzania Journal of Engineering and Technology 41, no. 1 (July 16, 2022): 10–18. http://dx.doi.org/10.52339/tjet.vi.768.

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There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholders useful insights that would help in improving HEIs’ effectiveness. Unfortunately, literature have identified several challenges associated with existing big data analytics frameworks in HEIs. It was on this line that the present study, which was based on desk research, was carried out to propose an effective big data framework for analytics of such data. The proposed framework is composed of five stages; data collection, data pre-processing, data storage, data analytics, and data visualization. The stages were arranged systematically to address the identified challenges in the existing frameworks. Effective implementation of this framework will help HEIs to make a productive use of various data they generate. This will ultimately be beneficial to not only HEIs but also to aspired students, labour market, the government and the public at large.
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Handayani, Erna, Siswoyo Haryono, and Akhmad Darmawan. "Transformation of entrepreneur education programs (EEPS) of Indonesia’s higher education." International Journal of Research in Business and Social Science (2147- 4478) 10, no. 1 (February 11, 2021): 180–88. http://dx.doi.org/10.20525/ijrbs.v10i1.976.

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The Entrepreneurship Education Program (EEPs) of Indonesia’s higher education has not been effective. The study aims to identify the extent to which EEPs in Indonesia was developed and propose several alternative schemes. The research uses the methodology of traditional narrative literature review and interviews with young entrepreneurs as the output of the EEPs higher education program in Indonesia. The analysis technique is done with an interactive model with steps of data collection, data reduction, data presentation, verification, and conclusions. The literature study is intended for the latest research information that evaluates the effect of EEPs on the formation of EM and the formation of new entrepreneurs. Furthermore, the information is verified by the results of the interview data processing. Entrepreneurship education for multidisciplinary students, business incubators, funding provision, and program sustainability studies is the result of research as a proposal for the development of higher education EEPs. This program is part of a long-term solution to addressing labor problems in Indonesia.
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Zhang, Xining. "Big Data Model of Higher Education Online Teaching Based on Intelligent Algorithm." Mobile Information Systems 2022 (August 25, 2022): 1–11. http://dx.doi.org/10.1155/2022/2492952.

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With the continuous development of society, the demand for processing large-scale data in many fields is increasing. Traditional processing training techniques have many limitations for big data analysis applications. Therefore, how to transform big data into general-purpose information becomes particularly important. This research mainly discusses the big data model analysis of higher education online teaching based on intelligent algorithms. The process of the experiment is to access how trainers interact or receive information stimulation in videos and courseware and how to cause relatively lasting changes in cognitive behavior. From the experimental research, we discovered the law of practical training and finally provided personalized teaching support services according to the needs and abilities of the trainers. On the other hand, the online training algorithm for big data analysis is studied, the methods needed to solve the big data mining task are discussed, and the online course training is recommended in many ways. Experimental data show that the algorithm of large-scale online training behavior data analysis on the behavior analysis results of online trainers is conducive to the improvement of online trainers’ learning efficiency. The experimental results show that the algorithm of large-scale online training behavior data analysis can show good model analysis performance, which is conducive to the prediction of the training personnel, and the prediction accuracy reaches about 90%. It is found that the algorithm that implements large-scale online training behavior data analysis can effectively categorize the relationship between the trainee’s visits. Through innovative data analysis methods, fast, efficient, and timely analysis of big data streams is realized.
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Said, Hanna Mohammad. "Data Mining and Knowledge Discovery in Big Data for Decision Making in Higher Education." Bioscience Biotechnology Research Communications 14, no. 4 (December 25, 2021): 2013–19. http://dx.doi.org/10.21786/bbrc/14.4.93.

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Artificial intelligence and data mining plays a fundamental role in improving the intelligence of education through special standards for improving teaching quality, better learning experience, predictive teaching, assessment method, effective decision-making, and improved data analysis. BD (Big Data) are also used to assess, detect, and anticipate decision-making, failure risk, and consequences to improve decision-making and maintain high-quality standards. According to the findings of this study, certain universities and governments have adopted BD to help students transition from traditional to smart digital education. Many obstacles remain in the way of complete adoption, including security, privacy, ethics, a scarcity of qualified specialists, data processing, storage, and interoperability. Learning today is getting smarter, thanks to the rapid development of the use of data and knowledge for big data analysis. Besides delivering real-world knowledge discovery applications, specialized data mining methodologies, and obstacles have real-world applications. Therefore, this article aims to explain the current concept of an intelligent learning environment in higher education. It explores the main criteria, and presents evaluation methods through the use of the proposed model.
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Dissertations / Theses on the topic "Data processing in higher education"

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Al-Mashaqbeh, Ibtesam. "Computer applications in higher education : a case study of students' experiences and perceptions." Virtual Press, 2003. http://liblink.bsu.edu/uhtbin/catkey/1263918.

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The purpose of this qualitative study was to describe the educational experiences with computers of nine female international graduate students at Ball State University. Their experiences with computers before they came to the United States, their current use of computers during their study at Ball State University, challenges faced related to the use of computers during their graduate study in the United States, and the support received from the university to help them overcome these barriers were described. Descriptions of ways computers supplemented and enriched the experiences of female international graduate students in the completion of their graduate work at Ball State University were reported.Participants of the present study were nine female international graduate students from Ball State University. They were identified through cooperation with the Center For International Programs, which provided a list of names and e-mail addresses of female international graduate students who were enrolled in graduate studies at Ball State University. Nine female international graduate students were selected from the list.The researcher interviewed each participant for two hours on one occasion. Following each interview participants were asked to complete a brief questionnaire to identify age, country of origin, academic program, and length of time spent in the United States.The following conclusions were established based upon this research study: (1) most participants did not use computer applications on a daily basis during their undergraduate study in their native countries; (2) all participants used computer applications on a daily basis during their study at BSU; (3) some participants faced two important academic adjustments at the same time, the adjustment to the English language and the adjustment to the use of computer; (4) most participants received support from friends regarding the use of computers; (5) most participants faced problems regarding their typing skills; (6) using the library web site was a challenge for most participants; (7) all participants believed that the use of computers enriched their experiences during their study at BSU; and (7) all participants used the Self-Learning Theory to improve their computer skills.
Department of Educational Studies
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Brotherton, Jason Alan. "Enriching everyday activities through the automated capture and access of live experiences : eClass: building, observing and understanding the impact of capture and access in an educational domain." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/8143.

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Bennett, Sandra M. "Exploring the relationship between continuing professional education and job satisfaction for information technology professionals in higher education." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5296/.

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The study had four main hypotheses that examined the relationships between job satisfaction and the reasons for attending continuing professional education (CPE). The purpose of this study was to examine the relationships between training and job satisfaction with the objective of adding to the body of knowledge related to both job satisfaction and training and development. Participation Reasons Scale was used to measure the reasons for attending CPE activities, and the Job in General Scale and Job Descriptive Index was used to measure job satisfaction. The surveys were administered over the Internet to information technology professionals working in higher education. The participants were contacted by email with a message explaining the purpose of the research and a Web link that took the participants directly to the survey. After collecting the data, it was exported into SPSS and analyzed using Spearman Rho and Mann Whitney U statistics and a simple structure exploratory factor to determine any underlying structures between the job satisfaction and CPE.
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Phipps, Owen Dudley. "The use of a database to improve higher order thinking skills in secondary school biology: a case study." Thesis, Rhodes University, 1994. http://hdl.handle.net/10962/d1003696.

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The knowledge explosion of the last decade has left education in schools far behind. The emphasis in schools must change if they are to prepare students for their future lives. Tertiary institutions as well as commerce and industry need people who have well-developed cognitive skills. A further requirement is that the school leaver must have skills pertaining to information processing. The skills that are required are those which have been labelled higher order thinking skills. The work of Piaget, Thomas and Bloom have led to a better understanding of what these skills actually are. Resnick sees these skills as being: nonalgorithmic; complex; yielding multiple solutions; involving nuanced judgements; involving the application of multiple criteria; involving uncertainty; involving self-regulation of the thinking process; imposing meaning and being effortful. How these can be taught and the implication of doing so are considered by the researcher. The outcome of this consideration is that higher order - thinking entails communication skills, reasoning, problem solving and self management. The study takes the form of an investigation of a particular case: whether a Biology field trip could be used as a source of information, which could be handled by a computer, so that higher order thinking skills could be acquired by students. Students were instructed in the use of a Database Management System called PARADOX. The students then went on an excursion to a Rocky Shore habitat to collect data about the biotic and abiotic factors pertaining to that ecosystem. The students worked in groups sorting data and entering it into the database. Once all the data had been entered the students developed hypotheses and queried the database to obtain evidence to substantiate or disprove their hypotheses. Whilst this was in progress the researcher obtained data by means of observational field notes, tape recordings, evoked documents and interviews. The qualitative data was then arranged into classes to see if it showed that the students were using any of the higher order thinking skills. The results showed that the students did use the listed higher order thinking skills whilst working on the database.
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Berry, William Lee. "The use of computer technology to compare and analyze community college dissertations." FIU Digital Commons, 1989. http://digitalcommons.fiu.edu/etd/1655.

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A purpose of this research study was to demonstrate the practical linguistic study and evaluation of dissertations by using two examples of the latest technology, the microcomputer and optical scanner. That involved developing efficient methods for data entry plus creating computer algorithms appropriate for personal, linguistic studies. The goal was to develop a prototype investigation which demonstrated practical solutions for maximizing the linguistic potential of the dissertation data base. The mode of text entry was from a Dest PC Scan 1000 Optical Scanner. The function of the optical scanner was to copy the complete stack of educational dissertations from the Florida Atlantic University Library into an I.B.M. XT microcomputer. The optical scanner demonstrated its practical value by copying 15,900 pages of dissertation text directly into the microcomputer. A total of 199 dissertations or 72% of the entire stack of education dissertations (277) were successfully copied into the microcomputer's word processor where each dissertation was analyzed for a variety of syntax frequencies. The results of the study demonstrated the practical use of the optical scanner for data entry, the microcomputer for data and statistical analysis, and the availability of the college library as a natural setting for text studies. A supplemental benefit was the establishment of a computerized dissertation corpus which could be used for future research and study. The final step was to build a linguistic model of the differences in dissertation writing styles by creating 7 factors from 55 dependent variables through principal components factor analysis. The 7 factors (textual components) were then named and described on a hypothetical construct defined as a continuum from a conversational, interactional style to a formal, academic writing style. The 7 factors were then grouped through discriminant analysis to create discriminant functions for each of the 7 independent variables. The results indicated that a conversational, interactional writing style was associated with more recent dissertations (1972-1987), an increase in author's age, females, and the department of Curriculum and Instruction. A formal, academic writing style was associated with older dissertations (1972-1987), younger authors, males, and the department of Administration and Supervision. It was concluded that there were no significant differences in writing style due to subject matter (community college studies) compared to other subject matter. It was also concluded that there were no significant differences in writing style due to the location of dissertation origin (Florida Atlantic University, University of Central Florida, Florida International University).
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Yusof, Normah. "An empirical study of the use of computer-based material for the teaching of the listening skill in English as a second language to students of a higher education institution in Malaysia." Thesis, University of Hull, 1999. http://hydra.hull.ac.uk/resources/hull:3881.

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The primary concern of the present study was to investigate the effective uses of computer-based software in teaching the listening skill in English as a Second Language (ESL) at higher education institutions. It aimed to find out if computer-based software can be used as a teacher replacement or supplement, either at the beginning or end of the listening lesson. This study also aimed to measure students' motivational reactions to instructional materials, to examine students' computer background, to examine any significant relationships between any of the variables and also to see if there are gender differences in any of these. In order to assess the effect of CALL use, an experimental study was carried out.The respondents consisted of 80 post-SPM students enrolled in Intensive English course at KUSZA, Malaysia. Two achievement post-tests and two sets of questionnaires were administered for data collection. The data results were analysed using SPSS (Statistical Package for Social Science) with statistical techniques of ANOVA, analysis of covariance, Pearson's product-moment correlation and t-tests for independent samples. The results of the study revealed that the way computerised material was used made a considerable effect on the achievement of the students. When it was used as a supplement at the beginning of the lesson, the students scored better results than those students received computerised treatment at the end of the lesson. When it was used as a teacher replacement, the students did not score well in the tests. The results also showed that non-computerised treatment was effective for teaching the listening skill.In the light of these results, certain recommendations were made for the teaching of the listening skill in ESL at KUSZA. It was recommended that computerised instruction be used as a supplement to teacher teaching at the beginning of the lesson rather than at the end of it. Further research is needed to be carried out with larger groups of respondents so that the findings can be generalised to other situations.
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Zhang, Zhidong 1957. "Cognitive assessment in a computer-based coaching environment in higher education : diagnostic assessment of development of knowledge and problem-solving skill in statistics." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102853.

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Diagnostic cognitive assessment (DCA) was explored using Bayesian networks and evidence-centred design (ECD) in a statistics learning domain (ANOVA). The assessment environment simulates problem solving activities that occurred in a web-based statistics learning environment. The assessment model is composed of assessment constructs, and evidence models. Assessment constructs correspond to components of knowledge and procedural skill in a cognitive domain model and are represented as explanatory variables in the assessment model. Explanatory variables represent specific aspects of student's performance of assessment problems. Bayesian networks are used to connect the explanatory variables to the evidence variables. These links enable the network to propagate evidential information to explanatory model variables in the assessment model. The purpose of DCA is to infer cognitive components of knowledge and skill that have been mastered by a student. These inferences are realized probabilistically using the Bayesian network to estimate the likelihood that a student has mastered specific components of knowledge or skill based on observations of features of the student's performance of an assessment task.
The objective of this study was to develop a Bayesian assessment model that implements DCA in a specific domain of statistics, and evaluate it in relation to its potential to achieve the objectives of DCA. This study applied a method for model development to the ANOVA score model domain to attain the objectives of the study. The results documented: (a) the process of model development in a specific domain; (b) the properties of the Bayesian assessment model; (c) the performance of the network in tracing students' progress towards mastery by using the model to successfully update the posterior probabilities; (d) the use of estimates of log odds ratios of likelihood of mastery as a measure of "progress toward mastery;" (e) the robustness of diagnostic inferences based on the network; and (f) the use of the Bayesian assessment model for diagnostic assessment with a sample of 20 students who completed the assessment tasks. The results indicated that the Bayesian assessment network provided valid diagnostic information about specific cognitive components, and was able to track development towards achieving mastery of learning goals.
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Jenkins-Todd, Derone I. (Derone Ilene). "Determination of Author Characteristics and Content of Educational Computing Articles in Community/Junior College Serials Literature, 1977-1991." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278735/.

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The study was undertaken: (a) to categorize the contents of educational computing articles using a taxonomy developed by Knezek, Rachlin, and Scannell (1988), (b) to examine the trends in educational computing subject matter addressed in community/junior college journals between 1977 and 1991, and (c) to identify and analyze specific characteristics of contributing authors and their employing institutions which might explain writing and publication biases.
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Jacobs, Carmen. "A framework for successful SOA adoption in selected South African universities." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1008366.

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The demand for systems i·ntegration has become more and more significant in higher education as institutions want systems that provide coherent information with data that is up to date and not redundant and can seamlessly support the end user experience. Institutions have become more reliant on information systems to support both administrative functions and those involved in teaching, learning and research, but because each department supports a diverse array of computing platforms and applications, it becomes very difficult to integrate these systems. SOA is classified as an innovative approach to integrating existing systems involving the use of independent services that can be accessed without knowledge of the underlying platform implementation. Unfortunately, the SOA initiative will not be success if it is not understood and used correctly by various applications and systems throughout the organisation. SOA introduces complexity and challenges in systems integration, acceptance, governance, data, development planning, security and external opportunities. If an organisation does not embrace or enable change in each of these areas, it is not ready for the adoption of SOA. This research investigates the systems integration challenge in selected South African universities and explores factors for SOA adoption. The framework for the adoption of SOA comprises seven factors, of which Systems Integration is the most significant and represents an efficient starting point for institutions considering SOA adoption. Acceptance, Governance, Data, Development Planning, Security and External Opportunities are other factors of SOA adoption that require careful and thorough consideration before an institution can successfully adopt SOA. The results of this research emphasise the importance of being able to embrace change and innovation and modify strategies in order to reflect the constant changes required for the adoption of SOA.
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Charoensri, Pijarn. "Technology Infusion in Career Services at U.S. Institutions of Higher Education in the Southwest." Thesis, University of North Texas, 1998. https://digital.library.unt.edu/ark:/67531/metadc278087/.

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The purpose of this study was to examine the use of computer and communications technologies at four-year public/private college and university career centers that are members of the Southwest Association of Colleges and Employers (SWACE). The findings of this study reveal that (a) all career centers are now using computer and telecommunications technologies for at least one office function; (b) small institutions do not use technologies as much as large institutions because they have fewer financial resources, less technical support from institutions, fewer personnel, and they also need time to learn to use technologies effectively; (c) public career centers are more willing to explore new technologies but private career centers mostly adopt and implement proven technologies; (d) career education does not utilize technologies as much as career counseling or job placement functions; (e) lack of financial resources and lack of technical support are major barriers to a technological infusion; and (f) technologies, including electronic student databases, computerized presentations with the network connection, and OCR scanners, will be needed in the near future.
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Books on the topic "Data processing in higher education"

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Savel'ev, A. Ya. Higher education and computerisation. Moscow: Progress Publishers, 1989.

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Savelʹev, A. I͡A. Higher education and computerisation. Moscow: Progress, 1989.

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International Conference on Software Engineering in Higher Education. Software engineering in higher education. Edited by King G. Southampton, UK: Computational Mechanics Publications, 1994.

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International Conference on Software Engineering in Higher Education (2nd 1995 Alicante, Spain). Software engineering in higher education II. Edited by Usó J. -L, Mitic P, and Sucharov L. J. Southampton, UK: Computational Mechanics Publications, 1996.

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Lane, Jason E. Building a smarter university: Big data, innovation, and analytics. Albany: State University of New York Press, 2014.

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Frankel, Martin M. Integrated Postsecondary Education Data System glossary. Washington, D.C: Center for Education Statistics, Office of Educational Research and Improvement, U.S. Dept. of Education, 1987.

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(Program), Nercomp. Journal of computing in higher education. Ashfield, Mass: Paideia Publishers, 1989.

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A, Shields Mark, ed. Work and technology in higher education: The social construction of academic computing. Hillsdale, N.J: Lawrence Erlbaum Associates, 1995.

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S, Lenth Charles, ed. Using national data bases. San Francisco, CA: Jossey-Bass, 1991.

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Diana, Oblinger, and Rush Sean C, eds. The learning revolution: The challenge of information technology in the academy. Bolton, Mass: Anker, 1997.

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Book chapters on the topic "Data processing in higher education"

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He, Linhua. "Innovation and Entrepreneurship Education in Higher Vocational Colleges Under Big Data." In Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), 979–84. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1468-5_113.

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Huang, Jui-Chan, and Kuo-Min Ko. "Cultivation of Innovative Talents in Higher Education Based on Big Data." In Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), 177–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1468-5_23.

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He, Linhua. "Innovation and Entrepreneurship Education in Higher Vocational Colleges Under the Background of “Internet+”." In Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), 1321–26. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1468-5_155.

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Bezrukov, Artem, and Dilbar Sultanova. "Data Processing and Visualization with Matlab: Introducing an IT Component to Training Chemical Engineers." In Mobility for Smart Cities and Regional Development - Challenges for Higher Education, 1048–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93907-6_111.

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Li, Jiang, and Xiaohua Ke. "Application of Big Data Technology to Ideological and Political Education in Institutions of Higher Learning." In Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), 505–15. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1468-5_62.

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Kurkovsky, Anatoly. "Sustainability, Big Data, and Local Community: A Simulation Case Study of a Growing Higher Education Institution." In Advances in Parallel & Distributed Processing, and Applications, 859–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69984-0_62.

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Proteasa, Viorel, and Andreea Vertes-Olteanu. "How to Cope with GDPR for Graduate Tracking: A Discussion at the Intersection of Law and Policy and Two Solutions." In Higher Education in Romania: Overcoming Challenges and Embracing Opportunities, 193–210. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94496-4_10.

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AbstractResearch on higher education, and not only, has used data collected for administrative purposes—register data, to answer various policy-relevant questions. The employability of university graduates is one of such questions, which have been pending, especially in those countries whose higher education massified in the last decades. The promise of register data relies on its objective nature and apparent low cost: researchers are basically processing data that is collected for administrative purposes. In spite of these advantages, register data is still underused. Data protection restrictions, especially the alignment to the General Data Protection Regulation, are reported to temper the thrust towards making administrative data available as micro-data for secondary use. We consider that the policy dialogue would benefit from a transdisciplinary exploration of the solutions found in the EU to counter such reservations to the use of register for research. We propose an analysis at the intersection of policy studies and law of two solutions found in the EU to make register data available to researchers. We attempt to address two related questions: how are the processes designed? How is the GDPR compliance put into practice? We used structured observations of primary and secondary literature to collect our data. Our aim is to enrich the debate surrounding register data as a basis for policy-relevant research and, pragmatically, to indicate policy solutions that can be easily adjusted to national contexts in the EU and put on the table of decision-makers.
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Pacheco, Renata Martins, Ariane Laurenti, Bruno Eduardo dos Santos Silva, Iascara Mattes, and Sara Meireles. "Diagnosis of Chemical and Special Waste Management in a Higher Education Institution: A Methodology for Data Acquisition and Processing." In World Sustainability Series, 205–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76885-4_14.

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Callaert, Julie, Elisabeth Epping, Gero Federkeil, Ben Jongbloed, Frans Kaiser, and Robert Tijssen. "Data Collection." In Higher Education Dynamics, 125–33. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3005-2_8.

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Jones, Kevin Anthony, and Ravi S. Sharma. "Data Model and Collection." In Higher Education 4.0, 103–19. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6683-1_5.

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Conference papers on the topic "Data processing in higher education"

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Gautam, Sidharth. "Role of Data Analysis in Higher Education." In 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2021. http://dx.doi.org/10.1109/ispcc53510.2021.9609509.

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Prodanov, Goran. "CODE OF CONDUCT FOR THE PROCESSING OF PERSONAL DATA IN HIGHER EDUCATION." In PROTECTION OF THE PERSONAL DATA AND THE DIGITALIZATION 2021. University publishing house "Science and Economics", University of Economics - Varna, 2021. http://dx.doi.org/10.36997/ppdd2021.172.

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The research objectives of this study are the legal framework, the practical benefits and the necessity for the development and possible adherence by controllers or processors of personal data in higher education to a Code of Conduct referred to in Article 40 of the GDPR. The study is part of an extended research at the time of development, related to the issues under consideration, which is aimed at developing a draft code. Its purpose is to provoke a discussion between stakeholders and interested parties, to be amended if necessary, and subsequently submitted for approval by the supervisory authority.
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Lee, Taerim. "A deep learning analytics to facilitate sustainability of statistics education." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19306.

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Deep Learning Analytics uses predictive models that provide actionable information. It is a multidisciplinary approach based on data processing, AI technology-learning enhancement, educational data mining, and visualization. The problem is that embracing DLA(Deep Learning Analytics) in evaluating data in higher education diverts educators’ attention from clearly identifying methods, benefits, and challenges of using DLA in higher education. Predictive models including random forest (RF), support vector machines (SVM), logistic regression (logistic), and Deep Learning were trained and their performances compared. The predicted value of “source of sustainability” and selected input variables were utilized to predict the drop out of learner. Expected significant outcomes and impact is that using DLA we can find the optimal learning management model for supporting services for instructors significantly impact the quality of statistics education and for learners is necessary to support announcements from instructors, for providing appropriate learning environments.
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Shtymak, Anatolii, Pavlo Mulesa, and Mykola Malyar. "Procedure for Determination of Professional Competence of a Higher Education Institution Graduate." In 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2020. http://dx.doi.org/10.1109/dsmp47368.2020.9204112.

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Verma, Seema, Jitendra Pandey, and Alavikunhu Panthakkan. "Data Analytics and Knowledge Management in a learning environment for Higher Education." In 2022 5th International Conference on Signal Processing and Information Security (ICSPIS). IEEE, 2022. http://dx.doi.org/10.1109/icspis57063.2022.10002568.

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Adshead, Deborah Alison. "Embedding Information Rights into Higher Education in the UK." In Third International Conference on Higher Education Advances. Valencia: Universitat Politècnica València, 2017. http://dx.doi.org/10.4995/head17.2017.5272.

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Estimates suggest that a data breach costs an organisation over $4 million and that the total global cost of breaches will soon exceed $2 trillion. Hacking is part of the problem but errors in processing by individuals are still the most reported reasons for data breaches in the UK. With predictions of more jobs involving data processing in the future and the exponential growth in accessible personal data, it is increasingly important that this problem is taken more seriously. This Information Commissioner’s Office (ICO) sponsored research constitutes stage one of a study into the teaching of Information Rights (IR) in HE across the UK including depth interviews with professional bodies and Pro Vice-Chancellors, and an online survey aimed at Heads of Departments. Though not widespread, the research found instances of IR being taught but some barriers to embedding it across relevant subjects do exist, including: • tutor expertise; • development and consistency of materials; and • competing curriculum development drivers. Most respondents recognised the increasing importance of IR and welcomed ICO support with this but also felt that despite the proposal of accreditation as a motivator to improve the situation buy-in would be more likely if it was part of overall management strategy.
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Suhre, Cor, Koos Winnips, Vincent De Boer, Pablo Valdivia, and Hans Beldhuis. "Students’ experiences with the use of a social annotation tool to improve learning in flipped classrooms." In Fifth International Conference on Higher Education Advances. Valencia: Universitat Politècnica València, 2019. http://dx.doi.org/10.4995/head19.2019.9131.

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To support the development and dissemination of more activating educational practices, pilot studies were launched on the use of the social annotation tool Perusall. During 2016-2017 several managers of higher education course units worked with Perusall in classes of a size varying from 10 to more than hundred students. To assess the usefulness and effectiveness of Perusall we focused on two related aspects (1) perception by students of Perusall as an appropriate tool to support the processing of the study texts and (2) the impact of student engagement in Perusall on students’ examination results. Student experiences were evaluated by means of online questionnaires about several aspects concerning to the adoption of Perusall and log data about student activities in the Perusall platform. The results of the study show that engagement in deep level processing of course content depends on three critical factors: the transparency of annotation assignments, the perceived ease of working in Perusall and teachers’ use of students’ annotations to discuss the course content during lectures. The study further shows that students receive better examination results the more they engage in the annotation assignments.
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Liao, Xiaoling. "Asset Management Mode of Higher Vocational Colleges Based on Big Data Technology." In CIPAE 2021: 2021 2nd International Conference on Computers, Information Processing and Advanced Education. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3456887.3456894.

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Barana, Alice, Luigi Di Caro, Michele Fioravera, Francesco Floris, Marina Marchisio, and Sergio Rabellino. "Sharing system of learning resources for adaptive strategies of scholastic remedial intervention." In Fourth International Conference on Higher Education Advances. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/head18.2018.8232.

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This paper presents a model for school remedial, focusing on improving the digital materials sharing process for the diversification of tutoring strategies. The model involves the characterization of materials for automatic assessment shared within a community of tutors. The characterization expects materials to be linked with natural language descriptors explicating their intended instructional objectives. The possibility of implementing a recommendation system on the basis of natural language processing techniques is discussed taking in consideration an analysis of the application of the model within a local-scale project. Clustering techniques searching for materials that have the same educational purposes but involve the activation of different cognitive processes are proposed, in order to continuously extend the database of shared materials in favour of the effectiveness of ongoing tutoring actions. The results collected from questionnaires submitted to students, tutors, and teachers involved in the project are shown, and clustering data are discussed highlighting the feasibility of the application of the model.
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Prieto Sanz, Helena. "Do text discussions improve the academic skills of students of HE? Andorra University case." In Sixth International Conference on Higher Education Advances. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/head20.2020.11211.

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Reading is a core competency in learning processes of higher education as a tool for accessing discipline-specific knowledge. The aim of this case study is to analyse the impact of text group discussions on the academic skills of students at the Universitat d’Andorra (UdA). Qualitative techniques -non-participant observation, interviews and discussion groups- were applied to UdA students and faculty. Five student groups belonging to the Bachelor of Teaching and Learning (BTL), Bachelor of Computer Science (BCS) and Bachelor of Business Administration (BBA) were studied. After processing the data with Atlas.ti, the first results were obtained. Evidences of a positive impact on the academic skills are identified. Firstly, both students and faculty indicated an improvement of the text comprehension mainly because of the peer interactions. Improvement of the critical and analitycal attitude, the own speech as well as the metacognitive learning are also highlighted as areas on which dialogic reading has positive impact.
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Reports on the topic "Data processing in higher education"

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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.
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Falfushynska, Halina I., Bogdan B. Buyak, Hryhorii V. Tereshchuk, Grygoriy M. Torbin, and Mykhailo M. Kasianchuk. Strengthening of e-learning at the leading Ukrainian pedagogical universities in the time of COVID-19 pandemic. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4442.

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Distance education has become the mandatory component of higher education establishments all over the world including Ukraine regarding COVID-19 lockdown and intentions of Universities to render valuable knowledge and provide safe educational experience for students. The present study aimed to explore the student’s and academic staff’s attitude towards e-learning and the most complicated challenges regarding online learning and distance education. Our findings disclosed that the online learning using Zoom, Moodle, Google Meet, BigBlueButton and Cisco has become quite popular among the students and academic staff in Ukraine in time of the lockdown period and beyond. Based on the Principal Component Analysis data processing we can conclude that students’ satisfaction and positive e-learning perception are in a good correlation with quality of e-learning resources and set of apps which are used while e-learning and distance education. Also, education style, methods, and manner predict willingness of students to self-study. The self-motivation, time-management, lack of practice, digital alienation, positive attitude towards ICT, and instruction strategy belong to the most important challenges of COVID-19 lockdown based on the students and academic staff interviews. Online learning on daily purpose should be used in the favor of strengthening of classical higher education rather than replacing the former. Blended education is the best alternative to face-to-face education, because the communication with mentor in a live environmental even virtual should have ushered the learners to complete online learning and improve its results.
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Chowdry, Haroon, Anna Vignoles, Alissa Goodman, Lorraine Dearden, and Claire Crawford. Widening participation in Higher Education: analysis using linked administrative data. IFS, June 2008. http://dx.doi.org/10.1920/re.ifs.2005.0069.

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Slade, Sharon. Applications of Student Data in Higher Education: Issues and Ethical Considerations. Ithaka S+R, September 2016. http://dx.doi.org/10.18665/283891.

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Slade, Sharon. Applications of Student Data in Higher Education: Issues and Ethical Considerations. Ithaka S+R, September 2016. http://dx.doi.org/10.18665/sr.283891.

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Radecki, Jane, and Rebecca Springer. Research Data Services in US Higher Education: A Web-Based Inventory. Ithaka S+R, November 2020. http://dx.doi.org/10.18665/sr.314397.

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Shalatska, Hanna, Olena Zotova-Sadylo, Olexandr Makarenko, and Larysa Dzevytska. Implementation of E-assessment in Higher Education. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4466.

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The purpose of the study is to develop the methodical recommendations for test-designers to cope with challenges of organizing on-line assessment process. In order to achieve this goal we have to reveal the strengths and weaknesses of e-assessment activities based on analysis of the universities practice. To find out students’ attitude to the issue of the study we have drawn up the questionnaire. The survey results confirm that e-assessment is rated as priority activity by learners thanks to individual approach to teaching/learning process, easily accessible and fast feedback, as well as interactive methods of implementation. Reviewing practical experience of Kryvyi Rih National University (KNU) and Donetsk Law Institute of the Ministry of Internal Affairs of Ukraine (DLI) and summarizing statistical data of the survey we have developed methodical support for designing e-assessment activities which involves: to determine intended recipients and the purpose of testing; select appropriate instruments and e-platform; specify forms of feedback; clarify the tasks; provide knowledge base for operating e-tests etc. Despite plenty of studies in this domain the test-designers and test-takers face to typical difficulties while operating e-assessment, and there is lack of research providing methodical practicalities in this field. To address learners’ and lecturers’ needs we have developed the methodical recommendations, which might be used in the drafting of e-assessment tests for students of different specialisms.
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Hinrichs, Peter L. State Appropriations and Employment at Higher Education Institutions. Federal Reserve Bank of Cleveland, November 2022. http://dx.doi.org/10.26509/frbc-wp-202232.

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This paper studies the impacts of state appropriations on staffing and salaries at public higher education institutions in the United States using employment and revenue data from the Integrated Postsecondary Education Data System, along with an instrumental variables strategy borrowed from Deming and Walters (2018) and Chakrabarti, Gorton, and Lovenheim (2020). The instrument sidesteps the potential endogeneity of state appropriations for a given institution in a given year by interacting an institution’s historical reliance on state appropriations with total state appropriations for all higher education institutions in a given year. The results suggest that higher state appropriations are associated with an increase in tenure-track assistant professors at four-year institutions. They are also associated with an increase in part-time instructional staff at both four-year and two-year institutions. However, they are not associated with a change in the number of tenured faculty. Appropriations are also positively related to salaries for a variety of employee groups, although notably not for instructional staff who are instructors, lecturers, or without an academic rank. Overall, the results show that public higher education institutions use state appropriations in a variety of ways, but I do not find evidence that they replace contingent faculty with tenured or tenure-track faculty when appropriations rise.
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Moretti, Enrico. Estimating the Social Return to Higher Education: Evidence From Longitudinal and Repeated Cross-Sectional Data. Cambridge, MA: National Bureau of Economic Research, August 2002. http://dx.doi.org/10.3386/w9108.

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Mayfield, Colin. Higher Education in the Water Sector: A Global Overview. United Nations University Institute for Water, Environment and Health, May 2019. http://dx.doi.org/10.53328/guxy9244.

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Higher education related to water is a critical component of capacity development necessary to support countries’ progress towards Sustainable Development Goals (SDGs) overall, and towards the SDG6 water and sanitation goal in particular. Although the precise number is unknown, there are at least 28,000 higher education institutions in the world. The actual number is likely higher and constantly changing. Water education programmes are very diverse and complex and can include components of engineering, biology, chemistry, physics, hydrology, hydrogeology, ecology, geography, earth sciences, public health, sociology, law, and political sciences, to mention a few areas. In addition, various levels of qualifications are offered, ranging from certificate, diploma, baccalaureate, to the master’s and doctorate (or equivalent) levels. The percentage of universities offering programmes in ‘water’ ranges from 40% in the USA and Europe to 1% in subSaharan Africa. There are no specific data sets available for the extent or quality of teaching ‘water’ in universities. Consequently, insights on this have to be drawn or inferred from data sources on overall research and teaching excellence such as Scopus, the Shanghai Academic Ranking of World Universities, the Times Higher Education, the Ranking Web of Universities, the Our World in Data website and the UN Statistics Division data. Using a combination of measures of research excellence in water resources and related topics, and overall rankings of university teaching excellence, universities with representation in both categories were identified. Very few universities are represented in both categories. Countries that have at least three universities in the list of the top 50 include USA, Australia, China, UK, Netherlands and Canada. There are universities that have excellent reputations for both teaching excellence and for excellent and diverse research activities in water-related topics. They are mainly in the USA, Europe, Australia and China. Other universities scored well on research in water resources but did not in teaching excellence. The approach proposed in this report has potential to guide the development of comprehensive programmes in water. No specific comparative data on the quality of teaching in water-related topics has been identified. This report further shows the variety of pathways which most water education programmes are associated with or built in – through science, technology and engineering post-secondary and professional education systems. The multitude of possible institutions and pathways to acquire a qualification in water means that a better ‘roadmap’ is needed to chart the programmes. A global database with details on programme curricula, qualifications offered, duration, prerequisites, cost, transfer opportunities and other programme parameters would be ideal for this purpose, showing country-level, regional and global search capabilities. Cooperation between institutions in preparing or presenting water programmes is currently rather limited. Regional consortia of institutions may facilitate cooperation. A similar process could be used for technical and vocational education and training, although a more local approach would be better since conditions, regulations and technologies vary between relatively small areas. Finally, this report examines various factors affecting the future availability of water professionals. This includes the availability of suitable education and training programmes, choices that students make to pursue different areas of study, employment prospects, increasing gender equity, costs of education, and students’ and graduates’ mobility, especially between developing and developed countries. This report aims to inform and open a conversation with educators and administrators in higher education especially those engaged in water education or preparing to enter that field. It will also benefit students intending to enter the water resources field, professionals seeking an overview of educational activities for continuing education on water and government officials and politicians responsible for educational activities
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