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Early Identification of students’ at risk/dropout

  • Job typeJob type : Peer to Peer
  • Job Duration03 to 06 months
  • Project LevelMedium Level

Project detail

Like many other areas of society and human activity, education has been significantly impacted by technological advancements, resulting in the emergence of many online learning platforms like Virtual Learning Environment (VLE) and Massive Open Online Courses (MOOC). These platforms provide numerous functionalities, but none incorporates a module that predicts students’ academic performance. Thus, education institutions, notably higher education, currently operate in a highly competitive and complex environment. These institutions regularly strive for improving their services by best faculty and technology-enabled educational platforms to achieve quality teaching and best student performance. Despite these advancements, institutions face increased student dropout rates, academic underachievement, graduation delays, and other persistent challenges.
Knowledge and timely assessment of factors affecting student learning can help educational institutions identify low-performing students. Thus, it enables plan many interventions such as student advising and policymaking for improving low-performing students during the learning process. Implementing appropriate interventions can help the student improve their performance and hence address the critical challenges of educational institutions. Several research efforts have been made, particularly computational efforts in assessing students’ performance in educational institutions. However, most research efforts focused on course checkpoints and grades and can predict students’ performance to non-satisfying accuracy level. These studies cannot identify the reasons for low performing students and hence fail to suggest appropriate remedial, particularly for students at-risk. Therefore, there is still confusion regarding the effectiveness of these studies in the early prediction of students’ performance.

This work aims to come up with solutions to detect students at-risk and drop out as early as possible, identify reasons behind low performance and suggest appropriate remedial for low-performing students by using advanced techniques.

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