Add to ORKGIn this paper, we developed predictive models based on students’ reviewing behaviors in an Introductory Programming course. These patterns were captured using an educational technology that students used to review their graded paper- based assessments. Models were trained and tested with the goal of identifying students’ academic performance and those who might be in need of assistance. The results of the retrospective analysis show a reasonable accuracy. This suggests the possibility of developing interventions for students, such as providing feedback in the form of effective reviewing strategies
Apart from being able to support the bulk of student activity in suitable disciplines such as comput...
We focus on grade prediction in the context of the 6.0001/2 course by utilizing student data – inclu...
This paper presents a new approach to automatically detect- ing lower-performing or “at-risk” studen...
In this paper, we developed predictive models based on students’ reviewing behaviors in an Introduct...
This paper presents a retrospective analysis of students’ use of self-regulated learning strategies ...
The high failure rates of many programming courses means there is a need to identify struggling stud...
abstract: Paper assessment remains to be an essential formal assessment method in today's classes. H...
abstract: This thesis investigates students' learning behaviors through their interaction with an ed...
This thesis describes a longitudinal study on factors which predict academic success in introductory...
This paper presents a Support Vector Machine predictive model to determine if prior programming know...
This Research Full Paper presents an end-to-end framework to enhance personalized programming learni...
Providing feedback to students is one of the most effective ways to enhance their learning. With the...
A model for predicting student performance on introductory programming modules is presented. The mod...
Different sources of data about students, ranging from static demographics to dynamic behavior logs,...
Prediction of student performance in Introductory programming courses can assist struggling students...
Apart from being able to support the bulk of student activity in suitable disciplines such as comput...
We focus on grade prediction in the context of the 6.0001/2 course by utilizing student data – inclu...
This paper presents a new approach to automatically detect- ing lower-performing or “at-risk” studen...
In this paper, we developed predictive models based on students’ reviewing behaviors in an Introduct...
This paper presents a retrospective analysis of students’ use of self-regulated learning strategies ...
The high failure rates of many programming courses means there is a need to identify struggling stud...
abstract: Paper assessment remains to be an essential formal assessment method in today's classes. H...
abstract: This thesis investigates students' learning behaviors through their interaction with an ed...
This thesis describes a longitudinal study on factors which predict academic success in introductory...
This paper presents a Support Vector Machine predictive model to determine if prior programming know...
This Research Full Paper presents an end-to-end framework to enhance personalized programming learni...
Providing feedback to students is one of the most effective ways to enhance their learning. With the...
A model for predicting student performance on introductory programming modules is presented. The mod...
Different sources of data about students, ranging from static demographics to dynamic behavior logs,...
Prediction of student performance in Introductory programming courses can assist struggling students...
Apart from being able to support the bulk of student activity in suitable disciplines such as comput...
We focus on grade prediction in the context of the 6.0001/2 course by utilizing student data – inclu...
This paper presents a new approach to automatically detect- ing lower-performing or “at-risk” studen...