Functionality
Functionality Scale is a crucial metric in evaluating eLearning tools. An ideal tool should support large-scale applications and cater to varied pedagogical approaches, from traditional lectures to interactive discussions. The impact of the tool on education should be analyzed to evaluate its results (Alvarez et al., 2022). Using evaluation is essential, but delving deeper into specific interface aspects causing confusion or non-intuitiveness would be advantageous.
Assistance
Tech Support and Help Availability is crucial for users. Expanding this category to assess the responsiveness of tech support, the comprehensiveness of help documentation, and community support like user forums can provide a comprehensive view of user assistance. Hypermediality, which focuses on communication channels and flexibility, could benefit from assessing the tool’s support for multimedia content creation (Yacouba, 2023).
Accessibility
Accessibility assessment is fundamental, but specifying the accessibility guidelines and the level of compliance can offer a more detailed picture of accessibility features. In the User-focused category, consider evaluating the tool’s support for diverse learning styles and abilities, including personalized learning paths and accommodations for differently-abled learners. Ensuring inclusivity is essential in educational technology.
Tech Requirements and Compatibility
Operating Systems and Web Browser evaluation should include cross-platform compatibility. The analysis of the performance of the tool on Windows, macOS, and Linux. Mobile Design Access assessment can be enhanced by evaluating the responsiveness of the tool’s design to different screen sizes and orientations, ensuring a seamless mobile learning experience. Privacy concerns are paramount in educational settings, especially the ones related to the digital world (Alwarafy et al., 2020).
Usability
In the Usage and Account Set Up category, evaluating data privacy during the sign-up process can address these concerns effectively. Cost of Use should specify which aspects of the tool are free and which require payment. It assesses whether free features are sufficient for meaningful educational use, helping educators make informed decisions.
Learning Analytics
Learning Analytics evaluation should consider the tool’s capacity for predictive analytics and learning path optimization, which can enhance positive learning outcomes (Ifenthaler & Yau, 2020). These advanced analytics can provide valuable insights for educators to enhance teaching and learning strategies.
References
Alvarez, R. P., Jivet, I., Pérez-Sanagustin, M., Scheffel, M., & Verbert, K. (2022). Tools designed to support self-regulated learning in online learning environments: a systematic review. IEEE Transactions on Learning Technologies, 15(4), 508-522. Web.
Alwarafy, A., Al-Thelaya, K. A., Abdallah, M., Schneider, J., & Hamdi, M. (2020). A survey on security and privacy issues in edge-computing-assisted internet of things. IEEE Internet of Things Journal, 8(6), 4004-4022. Web.
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: a systematic review. Educational Technology Research and Development, 68, 1961-1990. Web.
Yacouba, K. (2023). Malraux and Garréta Novel’s Space Between Narrativity and Hypermediality. Paradigmes, 6(3), 123-134. Web.