Table 2 Recent LA Intervention research with temporal student engagement analysis
Research | Context | ARS identification methods proposed/used | Platform implementation | Intervention/effectiveness evaluation |
|---|---|---|---|---|
Tempelaar et al. (2020) | Statistic Course 1027 students | Analyze temporal students' engagement (#Attempt, #Examples, #Hints, #Views, Time spent) by analyzing 3 time-phases after delivered each topic: 1) prepare for tutorial, 2) prepare for quiz, and 3) prepare for exam. Students who exhibited lower engagement in the early phases can be identified as ARS | Not available | Not available |
Foster and Siddle (2019) | Large scale, Open university | Send alert messages to students who had been inactive on the platform every 14Â days. The number of alert messages generated by each student served as an effective indicator for identifying at-risk students | Student dashboard | Alert message to student & personal tutor/Number of Alert messages has high prediction on fail students |
Cobos and Ruiz-Garcia (2020) | Web application course, 84 students | Cluster students weekly based on engagements (#Question, #VDO, #Attendance, Time on course) to identify ARS. Additional feature for instructors to record student interventions for tracking | LMS integration (edX) | Email/Student Interview with positive feedback |
Majumda et al. (2019) | Reading courses, 3 universities | Calculate score from student engagements (#Event, Total Time, Complete Rate, Unique Week/Day), classify students to 3 groups. The weaker group is identified as at-risk students | Student, instructor Dashboard, xAPI | Email/No evaluation reported |
Herodutou et al. (2019) | Large scale, Open university | Utilize machine learning methods with demographic and student engagement data (#access to forum, content, resource, wiki, glossary) to identify ARS that likely not submit the next assessment. The result reveals that students under the instructors that used the system gained higher scores | Instructor Dashboard | Upon instructor decision/No evaluation reported |
Şahin and Yurdugül (2019) | Computer Network Course, 79 students | Visualize student’s learning engagement and rank on the dashboard to enhance self-awareness and motivations. No ARS identification. Student interview results indicated the system is useful | Student dashboard LMS plugin (Moodle) | Not available |
Azcona et al. (2019) | CS1 & CS2 course, 266 students | Weekly predict ARS using Machine Learning methods. The prediction features include demographics (Age, Location, School GPA, previous course grade) and weekly engagements (%SolvedQuestion, #Lab work, #Attendance, Time spent, #Material access, Weekday access) | Customized VLE | Weekly message based on student engagement status/Evaluation based on score comparison from summative assessments |
Froissard et al. (2015) | Large scale, Public university | Instructors can customize engagement parameters to identify ARS including. The parameters include assessment activity, forum activity, gradebook and login. The system calculates risk score and display on instructor dashboard | Instructor dashboard LMS plugin (Moodle) | Email/No evaluation reported |
i-Ntervene (This research) | CS2 course, 253 students | Iteratively identify ARS in 2 aspects, learning engagement (#Attendance, #In-class activities, #Assignment, #Supplementary material access) and subject understanding (Assignment score) by analyzing temporal gap between what student performed and instructors’ expectation. The system provides temporal gap visualization in both individual students and class level | Instructor dashboard | Upon instructor decision/Intervention tracking/Systematic evaluation based on improvement of temporal engagement and assessment scores |