“Engagement vs. performance: using electronic portfolios to predict first semester engineering student retention,”
Abstract
As providers of higher education begin to harness the power of big data analytics, one very fitting application for these new techniques is that of predicting student attrition. The ability to pinpoint students who might soon decide to drop out of a given academic program allows those in charge to not only understand the causes for this undesired outcome, but it also provides room for the development of early intervention systems. While making such inferences based on academic performance data alone is certainly possible, we claim that in many cases there is no substantial correlation between how well a student performs and his or her decision to withdraw. This is specially true when the overall set of students has a relatively similar academic performance. To address this issue, we derive measurements of engagement from students' electronic portfolios and show how these features can be effectively used to augment the quality of predictions.
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Researchers have explored features such as students' prior experiences, motivation and home environment, and student engagement with the course for attrition prediction.
“To this end, researchers have explored features such as students' prior experiences, motivation and home environment (DeBoer et al., 2013) and student engagement with the course (Aguiar et al., 2014; Ramesh et al., 2014).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Aguiar et al., 2014
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works
Self-citation(1992) · Cited by 23,431 · see citing works
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
Leaving College: Rethinking the Causes and Cures of Student Attrition
Bean, J. P., & Tinto, V. (1988) · The Journal of Higher Education · doi:10.2307/1982243 · Cited by 7,408 · see citing works
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559 · see citing works
Data mining and knowledge discovery: making sense out of data
Feyyad, U. M. (1996) · IEEE Expert · doi:10.1109/64.539013 · Cited by 4,644 · see citing works
AN ESSAY TOWARDS SOLVING A PROBLEM IN THE DOCTRINE OF CHANCES
BAYES (1958) · Biometrika · doi:10.1093/biomet/45.3-4.296 · Cited by 2,939 · see citing works
Leaving College : Rethinking the Causes and Cures of Student Attrition /
Tinto, V. (2012) · Cited by 1,187 · see citing works
An essay towards solving a problem in the doctrine of chances
Bayes, T. (2003) · Resonance · doi:10.1007/bf02883540 · Cited by 987 · see citing works
College Student Retention: Formula for Student Success.
Seidman, A. (2005) · ScholarWorks (Walden University) · Cited by 750 · see citing works
Predictors of Academic Achievement and Retention among College Freshmen: A Longitudinal Study.
DeBerard, M. S., Spielmans, G. I., & Julka, D. L. (2004) · College student journal · Cited by 688 · see citing works
Predictors of First-Year Student Retention in the Community College
Fike, D. S., & Fike, R. (2008) · Community College Review · doi:10.1177/0091552108320222 · Cited by 416 · see citing works
Predicting Students Drop Out: A Case Study
Dekker, G. G., Pechenizkiy, M., & Vleeshouwers, J. J. (2009) · TU/e Research Portal · Cited by 381 · see citing works
How College Affects Students: Ten Directions for Future Research
Pascarella, E. T. (2006) · Journal of College Student Development · doi:10.1353/csd.2006.0060 · Cited by 355 · see citing works
Researching Electronic Portfolios and Learner Engagement: The REFLECT Initiative
Barrett, H. C. (2007) · Journal of Adolescent & Adult Literacy · doi:10.1598/jaal.50.6.2 · Cited by 352 · see citing works
A comparative analysis of machine learning techniques for student retention management
Delen, D. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.06.003 · Cited by 334 · see citing works
Xenos, M., Pierrakeas, C. J., & Pintelas, P. (2002) · Computers & Education · doi:10.1016/s0360-1315(02)00072-6 · Cited by 279 · see citing works
Zhang, G., Anderson, T. J., Ohland, M., & Thorndyke, B. (2004) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2004.tb00820.x · Cited by 266 · see citing works
Seidman, A. (2005) · Praeger eBooks · doi:10.5040/9781639736522 · Cited by 251 · see citing works
Hellinger distance decision trees are robust and skew-insensitive
Cieslak, D. A., Hoens, T. R., Chawla, N. V., & Kegelmeyer, W. P. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0222-1 · Cited by 246 · see citing works
Self-citationAstin, A. W., & Astin, H. S. (1992) · Cited by 227 · see citing works
Thammasiri, D., Delen, D., Meesad, P., & Kasap, N. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.07.046 · Cited by 205 · see citing works
"They shall be known by what they do": An activities-based typology of college students
Kuh, G. D., Hu, S., & Vesper, N. (2000) · Cited by 167 · see citing works
A Model for Freshman engineering r etention
Veenstra, C. P., & eriC, L. (2009) · AEE Journal · Cited by 149 · see citing works
Data Mining Applications: A comparative Study for Predicting Student's performancePreprint
Yadav, S. K., Bharadwaj, B., & Pal, S. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1202.4815 · Cited by 143 · see citing works
Herzog, S. (2006) · New Directions for Institutional Research · doi:10.1002/ir.185 · Cited by 138 · see citing works
Investigating university students’ adaptation to a digital learner course portfolio
López-Fernández, O., & Illera, J. L. R. (2008) · Computers & Education · doi:10.1016/j.compedu.2008.11.003 · Cited by 128 · see citing works
Predicting Student Attrition with Data Mining Methods
Delen, D. (2011) · Journal of College Student Retention: Research, Theory & Practice · doi:10.2190/cs.13.1.b · Cited by 124 · see citing works
Mendez, G., Buskirk, T. D., Lohr, S. L., & Haag, S. (2008) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2008.tb00954.x · Cited by 118 · see citing works
Burtner, J. A. (2005) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2005.tb00858.x · Cited by 112 · see citing works
Learning patterns of university student retention
Nandeshwar, A. R., Menzies, T., & Nelson, A. J. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.05.048 · Cited by 100 · see citing works
Li, Q., Swaminathan, H., & Tang, J. (2009) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2009.tb01033.x · Cited by 84 · see citing works
USE DATA MINING TO IMPROVE STUDENT RETENTION IN HIGHER EDUCATION - A Case Study
Zhang, Y., Oussena, S., Clark, T., & Kim, H. (2010) · International Conference on Enterprise Information Systems · doi:10.5220/0002894101900197 · Cited by 72 · see citing works
Mining academic data to improve college student retention
Lauría, E. J. M., Baron, J. D., Devireddy, M., Sundararaju, V., & Jayaprakash, S. M. (2012) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2330601.2330637 · Cited by 70 · see citing works
The Relationship between Electronic Portfolio Participation and Student Success
Knight, W. E., Hakel, M. D., & Gromko, M. H. (2006) · Cited by 41 · see citing works
Data mining for student retention management
Lin, S. (2012) · Journal of computing sciences in colleges · Cited by 41 · see citing works
Lin, J. J.-J., Imbrie, P. K., & Reid, K. (2009) · Cited by 37 · see citing works
Using E-Portfolios to Support an Undergraduate Learning Career: An Experiment with Academic Advising
Chen, H. L., & Black, T. C. (2010) · The EDUCAUSE quarterly/EDUCAUSE quarterly · Cited by 30 · see citing works
Comparison of data mining techniques used to predict student retention
Pittman, K. (2008) · NSUWorks (Nova Southeastern University) · Cited by 27 · see citing works
Effects of Living on Campus on African American Students' Educational Gains in College
Flowers, L. A. (2004) · NASPA Journal · doi:10.2202/0027-6014.1334 · Cited by 26 · see citing works
A Data-Mining Approach to Differentiate Predictors of Retention.
Yu, C. H., DiGangi, S. A., Jannasch-Pennell, A. K., Lo, W., & Kaprolet, C. M. (2007) · Cited by 24 · see citing works
Where are Learning Communities? Now: National Leaders Take Stock
MacGregor, J., & Smith, B. L. (2005) · About Campus Enriching the Student Learning Experience · doi:10.1002/abc.123 · Cited by 24 · see citing works
Using data mining to improve student retention in HE: a case study.
Zhang, Y., Oussena, S., Clark, T., et al. (2010) · SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University) · Cited by 22 · see citing works
The Blended Advising Model: Transforming Advising with ePortfolios.
Ambrose, G., & Ambrose, L. W. (2013) · International Journal of ePortfolio · Cited by 19 · see citing works
Self-citationElectronic Portfolios: Engaged Students Create Multimedia-Rich Artifacts
Ring, G. L., Weaver, B., & Jones, J. (2009) · Journal of the Research Center for Educational Technology · Cited by 18 · see citing works
Alkhasawneh, R. (2011) · VCU Scholars Compass (Virginia Commonwealth University) · doi:10.25772/6xxj-7q43 · Cited by 16 · see citing works
Predicting Student Retention and Academic Success at New Mexico Tech
Luna, J., & Gutjahr, A. L. (2007) · Cited by 10 · see citing works
Knight, W. E., Hakel, M. D., & Gromko, M. H. (2008) · Cited by 10 · see citing works
Reflection and electronic portfolios
Anthony, J. J. (2008) · doi:10.7273/000005845 · Cited by 7 · see citing works
Purpose, Audience, and Engagement in Spelman College's eFolio Project
Price, M. (2006) · IGI Global eBooks · doi:10.4018/978-1-59140-890-1.ch024 · Cited by 7 · see citing works
Integration of ePortfolios in a First-Year Engineering Course for Measuring Student Engagement
Goodrich, V., Aguiar, E., Ambrose, G., McWilliams, L. H., Brockman, J. B., & Chawla, N. V. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--20677 · Cited by 6 · see citing works
Self-citationAmbrose, G., Martin, H. E., & Page, H. R. (2014) · Cited by 3 · see citing works
Self-citationAN INTEGRATED PORTFOLIO AND ADVISING SYSTEM FOR UNDERGRADUATE ENGINEERING STUDENTS
Chung, E. C. Y., Younis, O., MAHDI, A. S., & Al‐Atabi, M. (2011) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 3 · see citing works
Data mining for student retention management
LinShieu-Hong (2012) · Journal of computing sciences in colleges · doi:10.5555/2167431.2167450 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Utilising learning analytics to support study success in higher education: a systematic review
Ifenthaler, D., & Yau, J. Y.-K. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09788-z · Cited by 915 · see citing works
Mining Big Data in Education: Affordances and Challenges
Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R. B., & Warschauer, M. (2020) · Review of Research in Education · doi:10.3102/0091732x20903304 · Cited by 454 · see citing works
Predicting academic performance: a systematic literature review
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018) · Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3293881.3295783 · Cited by 355 · see citing works
Learning Analytics in Higher Education—A Literature Review
Leitner, P., Khalil, M., & Ebner, M. (2017) · Studies in systems, decision and control · doi:10.1007/978-3-319-52977-6_1 · Cited by 263 · see citing works
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
Matz, S., Bukow, C. S., Peters, H., Deacons, C., Dinu, A., & Stachl, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-32484-w · Cited by 123 · see citing works
Kew, S. N., & Tasir, Z. (2021) · Technology Knowledge and Learning · doi:10.1007/s10758-021-09541-2 · Cited by 116 · see citing works
Predicting Student Academic Performance at Degree Level: A Case Study
Asif, R., Merceron, A., & Pathan, M. K. (2014) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2015.01.05 · Cited by 114 · see citing works
Early Prediction of Student Learning Performance Through Data Mining: A Systematic Review
López-Zambrano, J. H., Lara, J. A., & Romero, C. (2021) · Psicothema · doi:10.7334/psicothema2021.62 · Cited by 96 · see citing works
Mai, T. T., Bezbradica, M., & Crane, M. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.08.026 · Cited by 84 · see citing works
Implementing a Learning Analytics Intervention and Evaluation Framework: What Works?
Rienties, B., Cross, S., & Zdráhal, Z. (2016) · Springer eBooks · doi:10.1007/978-3-319-06520-5_10 · Cited by 81 · see citing works
Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy
Peña‐Ayala, A. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1243 · Cited by 65 · see citing works
Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review
Li, C., Herbert, N., Yeom, S., & Montgomery, J. (2022) · Education Sciences · doi:10.3390/educsci12110781 · Cited by 56 · see citing works
Buenaño-Fernández, D., Villegas-Ch, W. E., & Luján‐Mora, S. (2019) · Computer Applications in Engineering Education · doi:10.1002/cae.22100 · Cited by 53 · see citing works
Toward an Institutional Analytics Agenda for Addressing Student Dropout in Higher Education
Silva, L. M. H. D., Chounta, I., Rodríguez‐Triana, M. J., Roldan-Roa, E., Gramberg, A., & Valk, A. (2022) · Journal of Learning Analytics · doi:10.18608/jla.2022.7507 · Cited by 48 · see citing works
Popularity prediction of movies: from statistical modeling to machine learning techniques
Abidi, S. M. R., Xu, Y., Ni, J., Wang, X., & Wu, Z. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08546-5 · Cited by 45 · see citing works
A Human-Centered Review of Algorithms in Decision-Making in Higher Education
McConvey, K., Guha, S., & Kuzminykh, A. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580658 · Cited by 40 · see citing works
Stem-ming the Tide: Predicting STEM attrition using student transcript dataPreprint
Aulck, L. S., Aras, R., Li, L., L'Heureux, C., Lu, P., & West, J. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.09344 · Cited by 37 · see citing works
Analysing the predictive power for anticipating assignment grades in a massive open online course
Moreno-Marcos, P. M., Muñoz‐Merino, P. J., Alario‐Hoyos, C., Ayres, I. E., & Kloos, C. D. (2018) · Behaviour and Information Technology · doi:10.1080/0144929x.2018.1458904 · Cited by 35 · see citing works
Abidi, S. M. R., Hussain, M., Xu, Y., & Wu, Z. (2018) · Sustainability · doi:10.3390/su11010105 · Cited by 35 · see citing works
Student performance prediction based on data mining classification techniques
Saheed, Y. K., Oladele, T. O., Akanni, A. O., & Ibrahim, W. M. W. (2018) · Nigerian Journal of Technology · doi:10.4314/njt.v37i4.31 · Cited by 33 · see citing works
Al‐Samarraie, H., Sarsam, S. M., & Guesgen, H. W. (2016) · Behaviour and Information Technology · doi:10.1080/0144929x.2016.1186735 · Cited by 26 · see citing works
Hernández-Jiménez, M. T., Moreira-Mora, T. E., Solís, M., & Fernández-Martín, T. (2019) · Revista Educación · doi:10.15517/revedu.v44i1.37247 · Cited by 26 · see citing works
Kittur, J., Bekki, J., & Brunhaver, S. R. (2021) · Computer Applications in Engineering Education · doi:10.1002/cae.22479 · Cited by 25 · see citing works
Intelligent Predictive Analytics for Identifying Students at Risk of Failure in Moodle Courses
Anagnostopoulos, T., Kytagias, C., Xanthopoulos, T., Georgakopoulos, I., Salmon, I., & Psaromiligkos, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49663-0_19 · Cited by 24 · see citing works
Predicting Freshmen Attrition in Computing Science using Data Mining
Naseem, M., Chaudhary, K., & Sharma, B. N. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11018-3 · Cited by 23 · see citing works
Mining University Registrar Records to Predict First-Year Undergraduate Attrition.
Aulck, L. S., Nambi, D., Velagapudi, N., Blumenstock, J. E., & West, J. D. (2019) · Educational Data Mining · Cited by 22 · see citing works
Individualized help for at-risk students using model-agnostic and counterfactual explanations
Smith, B. I., Chimedza, C., & Bührmann, J. H. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10661-6 · Cited by 21 · see citing works
Peach, R. L., Greenbury, S. F., Johnston, I. G., Yaliraki, S. N., Lefevre, D. J., & Barahona, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-81709-3 · Cited by 20 · see citing works
Generalizing Predictive Models of Admission Test Success Based on Online Interactions
Moreno-Marcos, P. M., Laet, T. D., Muñoz‐Merino, P. J., Soom, C. V., Broos, T., Verbert, K., & Kloos, C. D. (2019) · Sustainability · doi:10.3390/su11184940 · Cited by 20 · see citing works
The Role of e-Portfolio for Smart Life Long Learning
Kwok, L. F., & Hui, Y. K. (2017) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-59454-5_11 · Cited by 19 · see citing works
Hall, M. M., Worsham, R. E., & Reavis, G. (2021) · Community College Review · doi:10.1177/0091552120982030 · Cited by 19 · see citing works
Nguyen, Q., Thorne, S., & Rienties, B. (2018) · Behaviormetrika · doi:10.1007/s41237-018-0060-1 · Cited by 14 · see citing works
Zarb, M., Alshaigy, B., Bouvier, D. J., Glassey, R. J., Hughes, J., & Riedesel, C. P. (2018) · doi:10.1145/3293881.3295780 · Cited by 14 · see citing works
Automatic Inspection of E-Portfolios for Improving Formative and Summative Assessment
Müller, W., Rebholz, S., & Libbrecht, P. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-52836-6_51 · Cited by 13 · see citing works
Ahn, D., & Chan, J. C. K. (2025) · Applied Cognitive Psychology · doi:10.1002/acp.70026 · Cited by 10 · see citing works
Kondisi Sosial Ekonomi Siswa Putus Sekolah
Sofya, R., Siwi, M. K., & Oknaryana, O. (2018) · Jurnal Inovasi Pendidikan Ekonomi (JIPE) · doi:10.24036/011024540 · Cited by 9 · see citing works
Predicting Student Retention Among a Homogeneous Population Using Data Mining
Bilquise, G., Abdallah, S., & Kobbaey, T. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-31129-2_4 · Cited by 9 · see citing works
An Analysis of Degree Curricula through Mining Student Records
Gottin, V. M., JIMÉNEZ, H. G., Finamore, A. C., Casanova, M. A., Furtado, A. L., & Nunes, B. P. (2017) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2017.54 · Cited by 7 · see citing works
Improving M-Learners’ Performance Through Deep Learning Techniques by Leveraging Features Weights
Adnan, M., Habib, A., Ashraf, J., Shah, B., & Ali, G. (2020) · IEEE Access · doi:10.1109/access.2020.3007727 · Cited by 7 · see citing works
Integration of ePortfolios in a First-Year Engineering Course for Measuring Student Engagement
Goodrich, V., Aguiar, E., Ambrose, G., McWilliams, L. H., Brockman, J. B., & Chawla, N. V. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--20677 · Cited by 6 · see citing works
Self-citationA Comparative Analysis of Selected Studies in Student Performance Prediction
Mogessie, M. (2017) · International Journal of Data Mining & Knowledge Management Process · doi:10.5121/ijdkp.2017.7402 · Cited by 6 · see citing works
A layered-analysis of the features in higher education data set
Gamie, E. A., El-Seoud, S. A., & Salama, M. A. (2019) · International Conference on Software and Information Engineering (ICSIE) · doi:10.1145/3328833.3328850 · Cited by 6 · see citing works
Qualitatively exploring electronic portfolios
Nwanganga, F., Aguiar, E., Ambrose, G. A., Goodrich, V., & Chawla, N. V. (2015) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2723576.2723651 · Cited by 6 · see citing works
Self-citationToward a platform to support vocational training of people with disabilities
Cardonha, C., Guimarães, R. L., Mattos, A. B., Nogima, J., Avegliano, P., Gallo, D. S., Herrmann, R., & Borger, S. (2015) · IBM Journal of Research and Development · doi:10.1147/jrd.2015.2453021 · Cited by 5 · see citing works
EXPLORING THE USEFULNESS OF ADAPTIVE ELEARNING LABORATORY ENVIRONMENTS IN TEACHING MEDICAL SCIENCE
Thai, T., & Polly, P. (2016) · Wiley eBooks · doi:10.1002/9781118998205.ch9 · Cited by 5 · see citing works
Analysis and Prediction of Students' Performance in a Computer-Based Course Through Real-Time Events
Uguina, L., Ayres, I. E., Fisteus, J. A., Alario‐Hoyos, C., & Kloos, C. D. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3331433 · Cited by 4 · see citing works
An international investigation into student concerns regarding transition into higher education
Zarb, M., Abandoh-Sam, J. A., Alshaigy, B., Bouvier, D. J., Glassey, R. J., Hughes, J., & Riedesel, C. P. (2018) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3197091.3205842 · Cited by 4 · see citing works
Hyperparameter Tuning Using Automated Methods to Improve Models for Predicting Student Success
Drăgulescu, B., & Bucos, M. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-59506-7_25 · Cited by 4 · see citing works
Predicting Student Retention Among a Homogeneous Population Using Data Mining
Bilquise, G., Abdallah, S., & Kobbaey, T. (2020) · Studies in big data · doi:10.1007/978-3-030-59338-4_13 · Cited by 4 · see citing works
Dropouts and Budgets: A Test of a Dropout Reduction Model among Students in Israeli Higher Education
Bar-Am, R., & Arar, O. (2017) · European Journal of Educational Research · doi:10.12973/eu-jer.6.2.134 · Cited by 4 · see citing works
Is Big Data Better? LMS Data and Prediction Accuracy in Postsecondary Education
Bird, K., Castleman, B., Song, Y., & Yu, R. (2025) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2024.2308306 · Cited by 3 · see citing works
Mining video data: Tracking learners for orchestration and design
Thompson, K., Howard, S. K., Yang, J. C., & Ma, J. (2016) · QUT ePrints (Queensland University of Technology) · Cited by 3 · see citing works
Where students go and how they do: Wi-Fi location data versus academic performance
Sarkar, S., Carpenter, B., Bader-El-Den, M. B., & Knight, A. (2016) · Conference on Human System Interactions · doi:10.1109/hsi.2016.7529607 · Cited by 2 · see citing works
Random Forest Missing Data Imputation Methods: Implications for Predicting At-Risk Students
Smith, B. I., Chimedza, C., & Bührmann, J. H. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-49342-4_29 · Cited by 2 · see citing works
Identifying Students at Risk and Beyond: A Machine Learning Approach
Aguiar, E. (2022) · Figshare · doi:10.7274/1v53jw8435h · Cited by 2 · see citing works
Self-citationHuang, F., & Feng, Z. (2024) · International Conference on Intelligent Education and Computer Technology (IECT) · doi:10.1145/3687311.3687413 · Cited by 1 · see citing works
Toirova, G. I., Kosimov, A., Fayziyeva, M., Topvoldiev, K., Akhmedovna, Y. T., & Turdaliev, D. (2025) · International Conference on Frontier Technologies and Solutions (ICFTS) · doi:10.1109/icfts62006.2025.11031749 · Cited by 1 · see citing works
RetractedAI-Based Modeling for Predicting Open University Student Retention
Jung, S. I., Lee, E. K., Lee, B. R., Kim, S., & Park, C. H. (2024) · Journal of Lifelong Learning Society · doi:10.26857/jlls.2024.5.20.2.27 · Cited by 1 · see citing works
Discovering learning antecedents in learning analytics literature
Kobayashi, V., Mol, S. T., & Kismihók, G. (2015) · UvA-DARE (University of Amsterdam) · Cited by 1 · see citing works
Bar-Am, R. (2017) · RePEc: Research Papers in Economics · Cited by 1 · see citing works
Detecting Students At-Risk Using Learning Analytics
Albassam, A. S. S. (2019) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · Cited by 1 · see citing works
Reflections on advocating visualization as part of an introductory programming
Pretorius, M. (2015)
Data-driven Methods for Course Selection and Sequencing
Morsy, S. (2019) · University of Minnesota Digital Conservancy (University of Minnesota)
Personalized Student Engagement Medium Recommendation System
Modak, M. M., Kadam, S., Desai, T., Dengle, R., & Gawade, V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3247-3_24
Unleashing the Power of Big Data and AI: Innovative Education for Tomorrow’s Learners
Hussain, H. N., & Mubarak, M. A. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-86708-8_15
EDUPULSE AI — INTELLIGENT STUDENT LIFECYCLE & ACADEMIC
(2026) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets100398
Hasan, M. M., Khan, M. R., Salam, M. A., & Rahman, M. M. (2026) · International Conference on Advanced Network Technologies and Applications (APAN) · doi:10.1109/apan70967.2026.11709714
Hoda, S. R. U., Atwell, E., & Baig, M. M. (2026) · Intelligent Data Analysis · doi:10.1177/1088467x261495737
- Authors
- Aguiar, E., Chawla, N. V., Brockman, J., Ambrose, G. A., & Goodrich, V.
- Affiliations
- University of Notre Dame (US)
- Venue
- Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
- Publisher
- ACM
- Published
- 2014-03-24
- Pages
- 103-112
- OpenAlex
- W2046993599
- Cited by
- 69
- References
- 55
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 7 · 2020: 9 · 2019: 9 · 2018: 9 · 2017: 8 · 2016: 5 · 2015: 4 · 2014: 1
- FWCI
- 3.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Demography › Evasion and Academic Success Factors
- Social Sciences › Education › Higher Education Research Studies
- Keywords
- student dropout prediction, student engagement, e-portfolio, academic performance, early intervention, higher education, big data analytics
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