A Heat Map Generation to Visualize Engagement in Classes Using Moodle Learning Logs

Dobashi, Konomu; Ho, Curtis; Fulford, Catherine P.; Lin, Meng-Fen Grace

doi:10.1109/incit.2019.8912068

Cited by 9 publications

(6 citation statements)

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“…Finally, concerning RQ5 (Do students in each collaborative group have different behavioural patterns in each teaching modality?) it has been found that the behavioural profiles within each collaborative group, that are represented in Heat Map, do not have a homogeneous pattern of interaction between the members of each collaborative group and that there are always one or two members in each group who set the pace of work (Dobashi et al, 2019;Sáiz-Manzanares et al, 2020b). Therefore, it can be concluded that monitoring the learning process in each student is essential throughout the entire development for the detection of students at risk, especially in the initial and intermediate phases of the learning process (Bannert et al, 2014;Bogarín et al, 2018;Cerezo et al, 2016;Sáiz-Manzanares et al, 2021b).…”

Section: Discussionmentioning

confidence: 99%

“…All visualisation options allow export in graphical format and in.csv format, for the elaboration of reports and their subsequent analysis with other tools. The use of visualisation techniques such as Heat Map for the detection of students at risk is a tool that is proving to be very effective (Dobashi et al, 2019;Sáiz-Manzanares, et al, 2020b). An example of student monitoring within a collaborative group can be found in Fig.…”

Section: Instrumentsmentioning

confidence: 99%

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Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques

et al. 2021

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Monitoring students in Learning Management Systems (LMS) throughout the teaching–learning process has been shown to be a very effective technique for detecting students at risk. Likewise, the teaching style in the LMS conditions, the type of student behaviours on the platform and the learning outcomes. The main objective of this study was to test the effectiveness of three teaching modalities (all using Online Project-based Learning -OPBL- and Flipped Classroom experiences and differing in the use of virtual laboratories and Intelligent Personal Assistant -IPA-) on Moodle behaviour and student performance taking into account the covariate "collaborative group". Both quantitative and qualitative research methods were used. With regard to the quantitative analysis, differences were found in student behaviour in Moodle and in learning outcomes, with respect to teaching modalities that included virtual laboratories. Similarly, the qualitative study also analysed the behaviour patterns found in each collaborative group in the three teaching modalities studied. The results indicate that the collaborative group homogenises the learning outcomes, but not the behaviour pattern of each member. Future research will address the analysis of collaborative behaviour in LMSs according to different variables (motivation and metacognitive strategies in students, number of members, interactions between students and teacher in the LMS, etc.).

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Section: Discussionmentioning

confidence: 99%

Section: Instrumentsmentioning

confidence: 99%

Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques

et al. 2021

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“…If LMSs included EDM and data visualisation techniques [21,33], this would make it easier to detect the student at risk. EDM procedures include different techniques, one of the simplest being frequency analysis with heat maps [34]. This is a visualisation technique that allows users to see the frequencies of the accesses to the different resources of the LMS in different colours, creating a heat map.…”

Section: Educational Data Mining (Edm) Proceduresmentioning

confidence: 99%

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

et al. 2021

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In this study, we used a module for monitoring and detecting students at risk of dropping out. We worked with a sample of 49 third-year students in a Health Science degree during a lockdown caused by COVID-19. Three follow-ups were carried out over a semester: an initial one, an intermediate one and a final one with the UBUMonitor tool. This tool is a desktop application executed on the client, implemented with Java, and with a graphic interface developed in JavaFX. The application connects to the selected Moodle server, through the web services and the REST API provided by the server. UBUMonitor includes, among others, modules for log visualisation, risk of dropping out, and clustering. The visualisation techniques of boxplots and heat maps and the cluster analysis module (k-means ++, fuzzy k-means and Density-based spatial clustering of applications with noise (DBSCAN) were used to monitor the students. A teaching methodology based on project-based learning (PBL), self-regulated learning (SRL) and continuous assessment was also used. The results indicate that the use of this methodology together with early detection and personalised intervention in the initial follow-up of students achieved a drop-out rate of less than 7% and an overall level of student satisfaction with the teaching and learning process of 4.56 out of 5.

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“…In this work, we opted for the Heat Map visualisation technique, since it provides the results with numerical and colour intensity visualisation throughout the course during the training activity. The use of visualisation techniques such as Heat Map is felt to be very useful to assess user behaviour in LMS ( Dobashi et al, 2019 ). The UBUMonitor application may be downloaded free at https://github.com/yjx0003/UBUMonitor .…”

Section: Methodsmentioning

confidence: 99%

Teacher Training Effectiveness in Self-Regulation in Virtual Environments

et al. 2022

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Higher education in the 21st century faces the challenge of changing the way in which knowledge is conveyed and how teachers and students interact in the teaching-learning process. The current pandemic caused by SARS-CoV-2 has hastened the need to face up to this challenge and has furthered the need to approach the issue from the perspective of digitalisation. To achieve this, it is necessary to design training programmes geared towards teaching staff and which address both the use of technology and instructional design aimed at promoting the development of self-regulated learning (SRL) and automatic feedback systems. In this study, work was carried out with 23 teachers (8 inexperienced and 15 experienced teachers) in a training programme conducted through Moodle. The aims were: (1) to test whether there were any significant differences between the behaviour patterns of new teachers compared to experienced teachers, (2) to determine whether clusters of behaviour patterns corresponded to the type of teacher and (3) to ascertain whether the level of teacher satisfaction with the training activity in digital teaching will depend on the type of teacher. A quantitative as well as a qualitative design was applied. Differences were found in the behaviour patterns in the training activities for the development of rubrics and use of learning analytics systems in virtual learning environments. It was also found that the type of teacher did not correspond exactly to the behaviour cluster in the learning platform. In addition, no significant differences were found in the level of satisfaction between the two kinds of teacher. The main contribution this study makes is to provide a detailed description of the training stage as well as the materials required for its repetition. Further analytical studies are required on teacher perception of training programmes in digital teaching in order to provide personalised training proposals that lead to an effective use of teaching in digital environments.

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A Heat Map Generation to Visualize Engagement in Classes Using Moodle Learning Logs

Cited by 9 publications

References 1 publication

Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques

Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

Teacher Training Effectiveness in Self-Regulation in Virtual Environments

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