Education Analytics: Challenges and Approaches

Zhang, Linlin; Li, Kin Fun

doi:10.1109/waina.2018.00086

Cited by 18 publications

(14 citation statements)

References 73 publications

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“…The idea has been presented previously by scholars from diverse perspectives. It is often explained using attributes such as student advising and monitoring [16]. Others articulated the performance model based on student educational outcome [17] and assessment of student' achievements [18].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

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Section: Related Workmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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“…The timely prediction of student performance enables the detection of low performing students, thus, empowering educators to intervene early during the learning process and implement the required interventions. Fruitful interventions include, but are not limited to, student advising, performance progress monitoring, intelligent tutoring systems development, and policymaking [5]. This endeavor is strongly boosted by computational advances in data mining and learning analytics [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

2020

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The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes. The electronic bibliographic databases searched include ACM, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. Eventually, we synthesized and analyzed a total of 62 relevant papers with a focus on three perspectives, (1) the forms in which the learning outcomes are predicted, (2) the predictive analytics models developed to forecast student learning, and (3) the dominant factors impacting student outcomes. The best practices for conducting systematic literature reviews, e.g., PICO and PRISMA, were applied to synthesize and report the main results. The attainment of learning outcomes was measured mainly as performance class standings (i.e., ranks) and achievement scores (i.e., grades). Regression and supervised machine learning models were frequently employed to classify student performance. Finally, student online learning activities, term assessment grades, and student academic emotions were the most evident predictors of learning outcomes. We conclude the survey by highlighting some major research challenges and suggesting a summary of significant recommendations to motivate future works in this field.

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“…Deep learning is mentioned because of its analytical and predictive ability, allowing the construction of an improved idea of the technology involved in educational processes. These results will allow the definition of variables related to teaching work, linked to several specific groups of students, and even covering an entire training project; according to Zhan [43], it has been achieved by incorporating data science tools for the analysis and improvement of educational processes.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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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.

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Education Analytics: Challenges and Approaches

Cited by 18 publications

References 73 publications

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

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

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