Applying Kano’s two-factor theory to prioritize learning analytics dashboard features for learning technology designers

Tobias Alexander Bang Tretow-Fish; Md Saifuddin Khalid

doi:10.30935/cedtech/14286

Tobias Alexander Bang Tretow-Fish ¹ ^* , Md Saifuddin Khalid ¹

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CONT ED TECHNOLOGY, Volume 16, Issue 2, Article No: ep496

https://doi.org/10.30935/cedtech/14286

Submitted: 29 August 2023, Published Online: 26 February 2024

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Abstract

Existing methods for software requirements elicitation, five-point Likert scales and voting methods for requirements prioritization, and usability and user experience evaluation methods do not enable prioritizing the learning analytics dashboard requirements. Inspired by management and product design field, this research applies Kano’s two-factor theory to prioritize the features of learning analytics dashboards (LADs) of adaptive learning platform (ALP) called Rhapsode^TM learner, based on students’ perceived usefulness to support designers’ decision-making. Comparing usability and user experience methods for evaluating LAD features, this paper contributes with the protocol and a case applying Kano method for evaluating the perceived importance of the dashboards in ALP. The paper applies Kano’s two-factor questionnaire on the 13 LADs features of Rhapsode^TM learner. Responses from 17 students are collected using a questionnaire, which is used to showcase the strength of the two-factor theory through five tabular and graphical techniques. Through these five tabular and graphical techniques, we demonstrate the application and usefulness of the method as designers and management are often carried away by the possibilities of insights instead of actual usefulness. The results revealed a variation in the categorization of LADs depending on the technique employed. As the complexity of the techniques increases, additional factors that indicate data uncertainty are gradually incorporated, clearly highlighting the growing requirement for data. In the case of RhapsodeTM learner platform, results based on the students responses show that 11 of 13 LADs being excluded due to low significance level in categorization (technique 1) and low response rate.

Keywords: adaptive learning platforms, Kano’s two-factor theory, learning analytics dashboard, design methods

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Citation

Tretow-Fish, T. A. B., & Khalid, M. S. (2024). Applying Kano’s two-factor theory to prioritize learning analytics dashboard features for learning technology designers. Contemporary Educational Technology, 16(2), ep496. https://doi.org/10.30935/cedtech/14286

Tretow-Fish, T. A. B., and Khalid, M. S. (2024). Applying Kano’s two-factor theory to prioritize learning analytics dashboard features for learning technology designers. Contemporary Educational Technology, 16(2), ep496. https://doi.org/10.30935/cedtech/14286

Tretow-Fish TAB, Khalid MS. Applying Kano’s two-factor theory to prioritize learning analytics dashboard features for learning technology designers. CONT ED TECHNOLOGY. 2024;16(2), ep496. https://doi.org/10.30935/cedtech/14286

Tretow-Fish, Tobias Alexander Bang, and Md Saifuddin Khalid. "Applying Kano’s two-factor theory to prioritize learning analytics dashboard features for learning technology designers". Contemporary Educational Technology 2024 16 no. 2 (2024): ep496. https://doi.org/10.30935/cedtech/14286

Tretow-Fish, Tobias Alexander Bang et al. "Applying Kano’s two-factor theory to prioritize learning analytics dashboard features for learning technology designers". Contemporary Educational Technology, vol. 16, no. 2, 2024, ep496. https://doi.org/10.30935/cedtech/14286

Tretow-Fish TAB, Khalid MS. Applying Kano’s two-factor theory to prioritize learning analytics dashboard features for learning technology designers. CONT ED TECHNOLOGY. 2024;16(2):ep496. https://doi.org/10.30935/cedtech/14286