Resource Access Patterns in Exam Preparation Activities

We present a system for a dynamic graphical representation of the interactions captured in educational online environments. The system goes beyond interaction between students and teachers, also addressing resource usage or any other entity for which it is possible to create a relation which binds two entities. By defining these relationships between pairs of entities in an online learning environment (Moodle, in our case) our tool creates a graph, where it is possible to apply techniques of social network analysis. This system brings up new possibilities for e-learning as a tool capable of helping the teacher assorting and illustrating the degree of participation and to find the implicit relations between participants, or participants and resources or events.

show abstract

“…Therefore, resource usage can also be displayed. This approach is in line with recent research, from [9] and [2], for example.…”

Section: Introductionsupporting

confidence: 86%

A System for Visualization and Analysis of Online Pedagogical Interactions

Rei

Figueira

Oliveira

2017

Proceedings of the 2017 1st International Conference on E-Education, E-Business and E-Technology

View full text Add to dashboard Cite

show abstract

“…Resource usage has been proposed as a method for clustering learning objects in a way to reflect their semantic similarity [3]. We take an approach like the one taken by Ziebarth et al [4] when they intended to understand if students portray the same behavior in respect to resource usage during exam preparation. Our proposal goes further in the direction of predicting their final results from usage patterns [6] [7].…”

Section: Resource Usage As a Graphmentioning

confidence: 99%

“…Our motivation for this research is to have an expert system based on previous experiences that can trigger alarms whenever the systems detects, with a high percentage of confidence, that a student is following a path will lead him to a failure, so that he/she can be helped to mitigate and solve the detected problem as early as possible, as proposed in [4].…”

Section: Introductionmentioning

confidence: 99%

Communication and resource usage analysis in online environments: An integrated social network analysis and data mining perspective

Figueira

2017

2017 IEEE Global Engineering Education Conference (EDUCON)

View full text Add to dashboard Cite

Predicting whether a student will pass or fail is one of the most important actions to take while giving lectures. Usually, the experienced teacher is able to detect problematic situations at early stages. However, this is only true for classes up to a hundred students. For bigger ones, automatic methods are needed. In this paper, we present a predictive system based on three criteria retrieved and computed from the logs of the learning management system. We built fast frugal decision trees to help predict and prevent student failures, using data retrieved from their resource usage patterns. Evaluation of the decision system shows that the system's accuracy is very high both in train and test phases, surpassing logistic regression and CART.

show abstract

“…Along with the above mentioned exploratory studies, other research focused on pattern detection and grade prediction have also been gaining relevance. This includes, for instance, mining Moodle logs to detect patterns among resource usage in order to profile students and behaviors [12], as well as creating synthetic variables that describe student behavior in resource usage in order to predict passing or failing [13].…”

Section: Introductionmentioning

confidence: 99%

Mining Moodle Logs for Grade Prediction

Figueira

2017

Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality

View full text Add to dashboard Cite

Research concerning mining data from learning management systems have been consistently been appearing in the literature. However, in many situations there is not a clear path on the data mining procedures that lead to solid conclusions. Therefore, many studies result in ad-hoc conclusions with insufficient generalization capabilities. In this article, we describe a methodology and report our findings in an experiment which one online course which involved more than 150 students. We used the Moodle LMS during the period of one academic semester, collecting all the interactions between the students and the system. These data scales up to more than 33K records of interactions where we applied data mining tools following the procedure for data extraction, cleaning, feature identification and preparation. We then proceeded to the creation of automatic learning models based on decision trees, we assessed the models and validate the results by assessing the accuracy of the predictions using traditional metrics and draw our conclusions on the validity of the process and possible alternatives. CCS ConceptsApplied computing → E-learning • Computing methodologies → Classification and regression trees.

show abstract

Resource Access Patterns in Exam Preparation Activities

Cited by 8 publications

References 9 publications

A System for Visualization and Analysis of Online Pedagogical Interactions

A System for Visualization and Analysis of Online Pedagogical Interactions

Communication and resource usage analysis in online environments: An integrated social network analysis and data mining perspective

Mining Moodle Logs for Grade Prediction

Contact Info

Product

Resources

About