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The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent…
The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students. The present study, motivated by the same encouragement, proposes a deep learning model…
The main objective of higher education is to provide quality education to students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a…
Colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potential for predictive analytics, two major barriers exist to their adoption in higher education: (a) the lack of…
Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their…
The OECD pointed out that the best way to keep students up to school is to intervene as early as possible [1]. Using education big data and deep learning to predict student's score provides new resources and perspectives for early…
Student performance prediction is one of the most important subjects in educational data mining. As a modern technology, machine learning offers powerful capabilities in feature extraction and data modeling, providing essential support for…
The constantly increasing rate at which scientific papers are published makes it difficult for researchers to identify papers that currently impact the research field of their interest. Hence, approaches to effectively identify papers of…
Student repetition in secondary education imposes significant resource burdens, particularly in resource-constrained contexts. Addressing this challenge, this study introduces a unified machine learning framework that simultaneously…
To enhance student learning, we demonstrate an experimental study to analyze student learning outcomes in online and in-class sections of a core data communications course of the Undergraduate IT program in the Information Sciences and…
While academic metrics such as transcripts and GPA are commonly used to evaluate students' knowledge acquisition, there is a lack of comprehensive metrics to measure their preparedness for the challenges of post-graduation life. This…
Matrix factorization from a small number of observed entries has recently garnered much attention as the key ingredient of successful recommendation systems. One unresolved problem in this area is how to adapt current methods to handle…
In educational technology and learning sciences, there are multiple uses for a predictive model of whether a student will perform a task correctly or not. For example, an intelligent tutoring system may use such a model to estimate whether…
Instructors have limited time and resources to help struggling students, and these resources should be directed to the students who most need them. To address this, researchers have constructed models that can predict students' final course…
Faculty hiring shapes the flow of ideas, resources, and opportunities in academia, influencing not only individual career trajectories but also broader patterns of institutional prestige and scientific progress. While traditional studies…
Blended courses that mix in-person instruction with online platforms are increasingly popular in secondary education. These tools record a rich amount of data on students' study habits and social interactions. Prior research has shown that…
The on-time graduation rate among universities in Puerto Rico is significantly lower than in the mainland United States. This problem is noteworthy because it leads to substantial negative consequences for the student, both socially and…
University course ranking forums are a popular means of disseminating information about satisfaction with the quality of course content and instruction, especially with undergraduate students. A variety of policy decisions by university…
Bibliographic metrics are commonly utilized for evaluation purposes within academia, often in conjunction with other metrics. These metrics vary widely across fields and change with the seniority of the scholar; consequently, the only way…
Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater collection of observations to achieve enough predictive ability.…