Name
Abstract | ||
|---|---|---|
Modeling student engagement in computer-based scientific inquiry learning environments presents two challenges. First, extracting the variables that represent a student's engagement in learning and defining the causal relationships among them can be difficult. Such variables are often implicit due to the unobservable nature of mental model. Second, identifying the evidence from student interaction log to infer a student's engagement level is also a major challenge. Such challenge stemmed mainly because students are granted the freedom to formulate and evaluate hypotheses in computer-based scientific inquiry learning environments, not all interactions can be useful to infer the student's engagement level. As such, the assumption that the frequency of interactions correlates with the level of student engagement can often be misleading. Therefore, this research work attempted to identify the variables of student engagement and to determine the Bayesian Network that can capture the causal relationships between the variables. In this study, two variations of Bayesian Network model were handcrafted with the prior probabilities learned using the interaction logs of 54 students. The predictive accuracy of proposed models were benchmarked against Naive Bayes, Decision Tree, and Support Vector Machine. The empirical findings showed that the Bayesian Network model with convergence arc directions outperformed other models, suggesting it is an optimal model for modeling student engagement in INQPRO. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SMC.2013.501 | SMC |
Keywords | Field | DocType |
belief networks,computer-based scientific inquiry learning environments,naive bayes,student modeling,bayesian network model,bayesian networks,bayes methods,computer-based scientific inquiry,student engagement level,scientific information systems,student interaction log,bayesian network,graphical user interfaces,optimal model,interactive learning environment,student engagement,mental model,student engagement modeling,causal relationship,computer aided instruction,engagement level | Convergence (routing),Data science,Decision tree,Naive Bayes classifier,Computer science,Support vector machine,Graphical user interface,Bayesian network,Student engagement,Artificial intelligence,Unobservable,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 2 | 0.35 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Choo-Yee Ting | 1 | 90 | 13.19 |
| Wei-Nam Cheah | 2 | 2 | 0.35 |
| Ho Chiung Ching | 3 | 13 | 4.35 |