Measuring social presence in online-based learning: An exploratory path analysis using log data and social network analysis

Norz, Lisa-Maria; Dornauer, Verena; Hackl, Werner O.; Ammenwerth, Elske

doi:10.1016/j.iheduc.2022.100894

Cited by 19 publications

(8 citation statements)

References 59 publications

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“…Fourth, to avoid overreliance on self-report data from surveys and interviews, other means of capturing LMS usage (e.g. social network analysis, log data) such as those used in previous studies should be explored (Norz et al, 2023). Lastly, given that the current study's focus was on the perspectives of teachers and students, researchers are encouraged to extend investigations on LMS use to include perceptions of other stakeholders in higher education institutions such as technical support teams, instructional designers, and administrators.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

An Assessment of Learning Management System Use in Higher Education: Perspectives from a Comprehensive Sample of Teachers and Students

Simon,

Jiang,

Fryer

et al. 2024

Tech Know Learn

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Learning Management Systems (LMSs) are underutilized in universities despite them being standard components of higher education. Given this, there is a need to gather the perspectives of students and teachers to examine how these key end-users engage with LMS and utilize its various features and functions. Such an endeavor could aid in addressing the barriers and in promoting the use of LMSs in online teaching and learning. As studies about LMS use with both teacher and student samples are limited, we simultaneously gathered both students’ and teachers’ perceptions of Moodle from all academic faculties at a public university in Hong Kong. Structured qualitative interviews were conducted with eighty-two participants (41 teachers, 41 students) via Zoom. Results suggest that Moodle, while previously viewed as merely a repository of course materials, transitioned from being a supplementary tool to a key information dissemination tool during the pandemic. Despite the availability of collaboration tools, majority of interactions still occur in alternative communication channels. Findings point to students’ and teachers’ need for familiarity, training, and a visually and functionally improved interface to enhance their experience in the LMS. We also found how content and pedagogy influenced how LMS was integrated into teaching, with frequency and manner of LMS use varying between faculties and departments. Given this, tailor-made information and support for each department are needed to maximize LMS use in classes. We suggest the conduct of dialogues involving multiple stakeholders (i.e., teachers, students, administrators, and technical support staff) so that higher education institutions can share best practices on how to improve LMS utilization to enhance the quality of teaching and learning in universities.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

An Assessment of Learning Management System Use in Higher Education: Perspectives from a Comprehensive Sample of Teachers and Students

Simon,

Jiang,

Fryer

et al. 2024

Tech Know Learn

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“…Importantly, other types of network analyses exist and have been applied to address research questions within the realm of learning analytics, such as social network analysis (e.g., Chen and Poquet, 2022;Dawson et al, 2014;Mallavarapu et al, 2022; EVALUATING THE CONTENT STRUCTURE OF INTELLIGENT TUTOR SYSTEMS 6 Norz et al, 2023;Saqr and López-Pernas, 2022;Stasewitsch et al, 2022), semantic network analysis (e.g., Chowdhury et al, 2024), or epistemic network analysis (e.g., Shaffer et al, 2016). The difference between these network analysis and psychological network analysis is that the edges within psychological network analysis are estimated (i.e., they represent the correlation between two variables) and are not directly obtained from the data (e.g., edges within social network analysis may represent the amount of communication between people and are thus directly obtained from the data).…”

Section: Psychological Network Analysismentioning

confidence: 99%

Evaluating the content structure of intelligent tutor systems-a psychological network analysis

Spitzer,

Bardach,

Strittmatter

et al. 2024

Preprint

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The adoption of intelligent tutoring systems (ITSs) worldwide has led to a considerable accumulation of process data as students interact with different learning topics within these systems. Typically, these learning topics are structured within ITSs (e.g., the fraction topic includes subtopics such as a fraction number line subtopic). However, there is a lack of methods that offer quick, data-driven insights into the content structure of ITSs, particularly through easily accessible visualizations. Here, we applied psychological network analysis to process data (230,241 students; 5,365,932 problem sets) from an ITS for learning mathematics to explore performance interdependencies between 40 different subtopics. We argue that the visualization of these content interdependencies allows for a quick empirical evaluation of the validity of the existing structuring of the respective learning content. These insights allow for deriving recommendations concerning potential changes in the ITS structure and are thus highly valuable for ITS developers. Our results are also relevant for researchers as the interdependencies illustrated through psychological network analysis can contribute towards a better understanding of the interplay between mathematical skills. Together, our results indicate that psychological network analysis represents a valuable data-driven method to evaluate and optimize ITSs.

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“…Outer model test analysis helps evaluate construct validity. By using path analysis, researchers can examine whether the indicators used to measure a particular variable effectively reflect the measured concept [29]. If these indicators consistently and significantly predict other variables in the model, it suggests a strong relationship and influence within the studied framework.…”

Section: Outer Model Test Analysismentioning

confidence: 99%

Beyond Smart Devices: Fostering Critical, Communication and Collaborative Thinking in IoT-Based Sensor and Actuator Competence Learning Outcomes

Baskoro,

Suprianto,

Anifah

et al. 2023

TEM Journal

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This research aims to explore the level of critical and collaborative thinking among university students in IoT-based sensor and actuator competence courses in Surabaya during 2022-2023. This study employs quantitative research methods and utilizes the structural equation modeling approach with partial least squares algorithm for analysis. This paper aims to explore the intricate connections between critical thinking, communication skills, collaboration skills, and learning outcomes in the context of sensor and actuator competence by examining the existing research and exploring practical implications. Respondents comprise of 360 electrical engineering students in Surabaya currently taking sensor and actuator competencies in the 2022/2023 school year. The findings had significant role of collaborative learning in enhancing the association between critical thinking and learning outcomes, particularly in the affective and psychomotor domains. Collaborative learning can be considered an effective strategy to reinforce the connection between critical thinking and academic achievement in these two domains. The implications of this study have the potential to guide educational institutions in designing and implementing effective strategies for fostering critical and collaborative thinking skills among students in IoT-related fields.

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Measuring social presence in online-based learning: An exploratory path analysis using log data and social network analysis

Cited by 19 publications

References 59 publications

An Assessment of Learning Management System Use in Higher Education: Perspectives from a Comprehensive Sample of Teachers and Students

An Assessment of Learning Management System Use in Higher Education: Perspectives from a Comprehensive Sample of Teachers and Students

Evaluating the content structure of intelligent tutor systems-a psychological network analysis

Beyond Smart Devices: Fostering Critical, Communication and Collaborative Thinking in IoT-Based Sensor and Actuator Competence Learning Outcomes

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