Data mining in educational technology classroom research: Can it make a contribution?

TLDR: The paper addresses and explains some of the key questions about the use of data mining in educational technology classroom research and illustrates how data mining can be used to advance educational software evaluation practices in the field of educational technology.

Abstract: The paper addresses and explains some of the key questions about the use of data mining in educational technology classroom research. Two examples of use of data mining techniques, namely, association rules mining and fuzzy representations are presented, from a study conducted in Europe and another in Australia. Both of these studies examine student learning, behaviors, and experiences within computer-supported classroom activities. In the first study, the technique of association rules mining was used to understand better how learners with different cognitive types interacted with a simulation to solve a problem. Association rules mining was found to be a useful method for obtaining reliable data about learners' use of the simulation and their performance with it. The study illustrates how data mining can be used to advance educational software evaluation practices in the field of educational technology. In the second study, the technique of fuzzy representations was employed to inductively explore questionnaire data. The study provides a good example of how educational technologists can use data mining for guiding and monitoring school-based technology integration efforts. Based on the outcomes, the implications of the study are discussed in terms of the need to develop educational data mining tools that can display results, information, explanations, comments, and recommendations in meaningful ways to non-expert users in data mining. Lastly, issues related to data privacy are addressed.

Figures

Table 2. Sequential rules for FD learners

Figure 2. Simulation run

Figure 1. The underlying model about immigration policy of the glass-box simulation

Table 6. Key factors and their descriptions

Table 4. Sequential rules for FI learners

Table 5. Frequent sequences of rules for FI learners

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Data mining in educational technology classroom research: can it make a contribution?" ?

The paper addresses and explains some of the key questions about the use of data mining in educational technology classroom research. Two examples of use of data mining techniques, namely, association rules mining and fuzzy representations are presented, from a study conducted in Europe and another in Australia. The study illustrates how data mining can be used to advance educational software evaluation practices in the field of educational technology. The study provides a good example of how educational technologists can use data mining for guiding and monitoring school-based technology integration efforts. Based on the outcomes, the implications of the study are discussed in terms of the need to develop educational data mining tools that can display results, information, explanations, comments, and recommendations in meaningful ways to non-expert users in data mining. 

Q2. What have the authors stated for future works in "Data mining in educational technology classroom research: can it make a contribution?" ?

General discussion and concluding remarks Based on the findings of the two studies discussed here, educational data mining can make a significant contribution to educational technology classroom research in terms of providing educational researchers with the tools to study teaching and learning. In regards to the second issue about selecting appropriate data mining techniques, the authors found it useful to experiment first with different techniques using different software tools before making a final decision. However, as it is also easily inferred from the analyses, employing data mining techniques can be a challenging endeavor raising some issues of concern. For data mining, these different data types need to be processed into a unified form that can be used for data mining. 

Q3. What are the main characteristics of glassbox simulations?

Glass-box simulations are tools that promote explorative modeling; that is, they allow students to test or explore models, but not to create their own models or modify existing ones (Clariana & Strobel, 2008). 

Q4. What was the purpose of the study?

All research participants were asked to interact with a glass-box simulation that wasspecifically developed for the purposes of this study, in order to solve a problem about immigration policy. 

Q5. What were the main findings of the study?

The study was undertaken to examine which factors of students’ technology integration, such as positive and negative engagement, and high and low confidence in using digital technologies were meaningfully related to learning outcomes. 

Q6. What are the two examples of use of data mining techniques?

Two examples of use of data mining techniques, namely, association rules mining and fuzzy representations are presented, from a study conducted in Europe and another in Australia. 

Q7. What was the minimum support for the two sequence, association, and link analyses?

For the two sequence, association, and link analyses that were performed, the minimum support was set to 0.55 and the confidence level to 0.95. 

Q8. What is the purpose of the study?

The research purpose of the study was to identify sequences of interactions with the simulation that were associated with successful performance and whether those sequences of interactions differed between FD and FI learners. 

Q9. What is the main point of the second study?

In essence, the second study directly addresses the complexity of technology integration,which according to Borko, Whitcomb, and Liston (2009), has proven to be a “wicked” problem for educational research. 

Q10. What software was used to capture the students’ interactions with the immigration problem?

The students’ interactions with the simulation were captured into video files with RiverPast Screen Recorder, a screen capturing software.