Critical Review of Technology-Enhanced Learning using Automatic Content Analysis

Technology-enhanced learning (TEL) continues to grow gradually while considering a multitude of factors, which underpins the need to develop a TEL maturity assessment as a guideline for this gradual improvement. This study investigates the potential application of TEL’s expert knowledge presented in various research articles as qualitative data for developing assessment questionnaires. A mixed-method approach is applied to analyze the qualitative data using systematic literature review (SLR) with automated content analysis (ACA) as quantitative data processing to strengthen the trustworthiness of the findings and reduce researcher bias. This process is carried out six steps: conducting SLR, data processing with ACA using Leximancer, organizing resulting concepts with facet analysis, contextualizing each TEL facet, constructing the assessment questionnaire for each context, and establishing TEL maturity dimensions. This study generates 64 questionnaire statements grouped according to the target respondents, namely students, teachers, or institutions. This set of questions is also grouped into dimensions representing aligned context: student performance, learning process, applied technology, contents, accessibility, teachers and teachings, strategy and regulation. Further research is required to distribute this questionnaire for pilot respondents to design the improvement roadmap and check data patterns to formulate maturity appraisals and scoring methods.

@article{Rahmah2022,
  title     = {Critical Review of Technology-Enhanced Learning using Automatic Content Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {1},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Amalia Rahmah and Harry B. Santoso and Zainal A. Hasibuan},
  doi       = {10.14569/IJACSA.2022.0130148},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130148}
}