Using neural networks to predict the future performance of students

Albuquerque, Rosangela Marques de; Bezerra, Andre Alves; Souza, Darielson A.; Nascimento, Luís; Mesquita, Jarbas Joaci de; Nascimento, José Cláudio do

doi:10.1109/siie.2015.7451658

Cited by 16 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the training phase, we supplied the inputs and the corresponding data classes to the ML classifier to allow the classifiers to discover the patterns between the input and output [ 73 ]. Finally, the trained models used these patterns to classify unseen data [ 73 ].…”

Section: Methodsmentioning

confidence: 99%

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Hussain

Zhu

Zhang

et al. 2018

Computational Intelligence and Neuroscience

251

167

View full text Add to dashboard Cite

Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level, final results, score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which included dataplus, forumng, glossary, oucollaborate, oucontent, resources, subpages, homepage, and URL during the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.

show abstract

Section: Methodsmentioning

confidence: 99%

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Hussain

Zhu

Zhang

et al. 2018

Computational Intelligence and Neuroscience

251

167

View full text Add to dashboard Cite

show abstract

“…Only through understanding both what works and what does not can we form a holistic understanding of the topic. [7, 22, 46, 47, 62, 63, 71-73, 83, 87, 89, 94, 104, 107, 147, 149, 166, 172, 173, 177, 184, 186, 206, 208, 215, 218, 220, 231, 232, 244, 279, 296, 299, 315, 319, 327, 329, 337, 341, 358, 359, 373, 374, 407, 411, 424] Exam / Post-test Grade or Score [9,10,21,24,33,35,52,59,67,68,77,80,81,85,90,96,109,114,116,127,136,152,162,163,169,193,195,199,202,205,214,217,224,233,238,241,270,274,275,277,284,287,301,302,309,314,…”

Section: Calls To the Communitymentioning

confidence: 99%

Predicting academic performance: a systematic literature review

Hellas

Ihantola

Petersen

et al. 2018

Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education

231

171

View full text Add to dashboard Cite

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

show abstract

“…Regarding the application of deep learning, its importance lies in allowing-through quantitative techniques, designing, and managing-the strategies necessary to transform data into key, essential, optimal, productive, and scalable information, to allow better decision-making in the present [42].…”

Section: Discussionmentioning

confidence: 99%

Teaching Challenges in COVID-19 Scenery: Teams Platform-Based Student Satisfaction Approach

et al. 2020

View full text Add to dashboard Cite

At the onset of the crisis caused by COVID-19, the Mexican education system chose to join the global context and suspend face-to-face classes for all educational levels. For the continuity of educational processes, a transition from a traditional educational model (face-to-face) to emergency remote teaching (ERT) was made through virtual learning platforms and learning management system (LMS) schemes. Universidad del Valle de Mexico (UVM), in a collaboration agreement with Microsoft Co., chose to use Teams to continue its educational process. In this work, we intend to identify the factors that can be taken into account regarding the level of student satisfaction in the teaching–learning process in ERT using Teams, and validate the established educational strategy. Statistical analysis was carried out to analyze the academic environment for these scenario changes while considering knowledge assessment, and competencies achievement. A combined sampling method was applied with convenience and statistical analysis. The main results established significant percentages, where more than 60% of the students surveyed were manifested in the use of the teams and the organization of the class sessions by the teachers, and the activities developed. Using the Cronbach’s Alpha coefficient, the reliability of the data collection instruments was determined. The correlations of each of the survey questions were calculated to determine the relationship between themselves and the total answers, giving results similar to those obtained through data science tools. Taking advantage of the situation, data science tools were applied to compare the results with obtained values from RapidMiner software in the correlation of factors in of 0.440, 0.384, 0.246, 0.048 and 0.384.

show abstract

Using neural networks to predict the future performance of students

Cited by 16 publications

References 5 publications

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Predicting academic performance: a systematic literature review

Teaching Challenges in COVID-19 Scenery: Teams Platform-Based Student Satisfaction Approach

Contact Info

Product

Resources

About