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Description
Learning analytics application is evolving into a student-facing solution. Student-facing learning analytics dashboards (SFLADs), as one popular application, occupies a pivotal position in online learning. However, the application of SFLADs faces challenges due to teacher-centered and researcher-centered approaches. The majority of SFLADs report student learning data to teachers, administrators, and

Learning analytics application is evolving into a student-facing solution. Student-facing learning analytics dashboards (SFLADs), as one popular application, occupies a pivotal position in online learning. However, the application of SFLADs faces challenges due to teacher-centered and researcher-centered approaches. The majority of SFLADs report student learning data to teachers, administrators, and researchers without direct student involvement in the design of SFLADs. The primary design criteria of SFLADs is developing interactive and user-friendly interfaces or sophisticated algorithms that analyze the collected data about students’ learning activities in various online environments. However, if students are not using these tools, then analytics about students are not useful. In response to this challenge, this study focuses on investigating student perceptions regarding the design of SFLADs aimed at providing ownership over learning. The study adopts an approach to design-based research (DBR; Barab, 2014) called the Integrative Learning Design Framework (ILDF; Bannan-Ritland, 2003). The theoretical conjectures and the definition of student ownership are both framed by Self-determination theory (SDT), including four concepts of academic motivation. There are two parts of the design in this study, including prototypes design and intervention design. They are guided by a general theory-based inference which is student ownership will improve student perceptions of learning in an autonomy-supportive SFLAD context. A semi-structured interview is used to gather student perceptions regarding the design of SFLADs aimed at providing ownership over learning.
ContributorsLi, Siyuan (Author) / Zuiker, Steven (Thesis advisor) / Cunningham, James (Committee member) / Lande, Micah (Committee member) / Arizona State University (Publisher)
Created2019
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Description
One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in

One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in MOOC. There are different approaches and various features available for the prediction of student’s dropout in MOOC courses.In this research, the data derived from the self-paced math course ‘College Algebra and Problem Solving’ offered on the MOOC platform Open edX offered by Arizona State University (ASU) from 2016 to 2020 was considered. This research aims to predict the dropout of students from a MOOC course given a set of features engineered from the learning of students in a day. Machine Learning (ML) model used is Random Forest (RF) and this model is evaluated using the validation metrics like accuracy, precision, recall, F1-score, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) curve. The average rate of student learning progress was found to have more impact than other features. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5% respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. The features engineered in this research are predictive of student dropout and could be used for similar courses to predict student dropout from the course. This model can also help in making interventions at a critical time to help students succeed in this MOOC course.
ContributorsDominic Ravichandran, Sheran Dass (Author) / Gary, Kevin (Thesis advisor) / Bansal, Ajay (Committee member) / Cunningham, James (Committee member) / Sannier, Adrian (Committee member) / Arizona State University (Publisher)
Created2021