Conceptualizing Technology-Enhanced Learning Constructs: A Journey of Seeking Knowledge Using Literature-Based Discovery

Rahmah, Amalia; Santoso, Harry Budi; Hasibuan, Zainal A.

doi:10.1007/978-3-030-52249-0_50

Cited by 4 publications

(7 citation statements)

References 6 publications

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“…However, this study also has a limitation in that it processed only 100 articles; therefore, its suitability for this research objective is dubious. Another study has already employed ACA [19], however, a drawback in the work is a lack of clarity on interpreting the results. In that study, the dimensions were directly derived from themes generated from Leximancer without further analysis, yet the underlying knowledge lies in the concepts generated.…”

Section: B Slr With Aca To Gain the Whole Scope Of Tel Discussionmentioning

confidence: 99%

“…Such outliers are inappropriate for this study, which looks for concepts and dimensions capable of representing TEL. In the previous work [19], the selection of candidate dimensions was done only by the highest score concepts.…”

Section: B Aca With Leximancermentioning

confidence: 99%

“…Table III describes the dimensions and the corresponding assessment questionnaire statements. In previous work [19], the result only consists of three general dimensions: technology advancement, improved learning design, student's achievement. The number of questionnaire statements for each dimension is determined by general statistical provisions stating that the number of related questions should not be excessively different.…”

Section: Tel Maturity Assessment Dimensionsmentioning

confidence: 99%

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Critical Review of Technology-Enhanced Learning using Automatic Content Analysis

Rahmah¹,

Santoso²,

Hasibuan³

2022

IJACSA

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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.

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Section: B Slr With Aca To Gain the Whole Scope Of Tel Discussionmentioning

confidence: 99%

Section: B Aca With Leximancermentioning

confidence: 99%

Section: Tel Maturity Assessment Dimensionsmentioning

confidence: 99%

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Critical Review of Technology-Enhanced Learning using Automatic Content Analysis

Rahmah¹,

Santoso²,

Hasibuan³

2022

IJACSA

Self Cite

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“…Another study [24] also applied text mining and proposed a recommender system for an intelligent chatbot. Another study applied text mining in a different context, Rahmah et al [18] conducted literature-based-discovery that combined literature review and text mining to find Technology-Enhanced Learning components.…”

Section: Text Miningmentioning

confidence: 99%

“…This study uses a different approach from the previous studies and results in various student factors. While the previous research using Kitchenham's Systematic Literature Review (SLR), this study applies the strategy of seeking knowledge using literature-based discovery [18], then analyzing using quantitative and qualitative approach. This study aims to describe factors affecting students' academic achievement using a co-occurrence analysis and using facet analysis to propose a conceptual model related to big data learning analytics.…”

Section: Introductionmentioning

confidence: 99%

Finding Contributing Factors of Students’ Academic Achievement Using Quantitative and Qualitative Analyses-Based Information Extraction

Yunita

Santoso

Hasibuan

2022

Int. J. Emerg. Technol. Learn.

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Big data learning analytics is still in its infancy and has been developed on several campuses worldwide. Ideally, all students' profiles should be described and embraced to optimize the development of any proposed system related to big data learning analytics. This paper aims to extract information related to factors contributing to students’ academic achievement using quantitative and qualitative approach, in which co-occurrence analysis were applied for quantitative approach and facet analysis for the qualitative approach. For data collection, Kitchenham’s technique were used to select and filter the literature, at the first iteration, 1,167 papers were found. After applying inclusion and exclusion criteria, 101 articles were processed for text mining. Titles and abstracts were analyzed using a text-mining tool, and then resulted clusters of words. Afterwards, clusters of words were labeled using facet analysis. This study results in eight interrelated clusters of academic achievement factors: demography, internal consistency, technology, student course engagement, activity in a classroom, educational system, socio-culture, and personality. Several insights into each cluster will be described and might be beneficial for researchers in learning analytics.

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Towards Using Universal Big Data in Artificial Intelligence Research and Development to Gain Meaningful Insights and Automation Systems

Hasibuan

2020

2020 International Workshop on Big Data and Information Security (IWBIS)

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Conceptualizing Technology-Enhanced Learning Constructs: A Journey of Seeking Knowledge Using Literature-Based Discovery

Cited by 4 publications

References 6 publications

Critical Review of Technology-Enhanced Learning using Automatic Content Analysis

Critical Review of Technology-Enhanced Learning using Automatic Content Analysis

Finding Contributing Factors of Students’ Academic Achievement Using Quantitative and Qualitative Analyses-Based Information Extraction

Towards Using Universal Big Data in Artificial Intelligence Research and Development to Gain Meaningful Insights and Automation Systems

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