Subtopic Deep Dive
Qualitative Data Analysis in Edtech Evaluation
Research Guide
What is Qualitative Data Analysis in Edtech Evaluation?
Qualitative Data Analysis in Edtech Evaluation applies thematic analysis, interviews, and case studies to assess user experiences, tool efficacy, and trustworthiness in educational technology implementations.
Researchers use methods like thematic coding of teacher interviews and student feedback to evaluate edtech during disruptions such as COVID-19. Over 10 papers from 2013-2022, including Lukas and Yunus (2021) with 111 citations, document challenges in e-learning adoption. Frameworks combine qualitative insights with fuzzy logic for robust evaluation.
Why It Matters
Qualitative analysis reveals ESL teachers' barriers to e-learning, as in Lukas and Yunus (2021), enabling targeted interventions for better adoption. It identifies motivation gaps affecting performance, per Mauliya et al. (2020), informing edtech designs that boost engagement. In needs assessments like Ardianti et al. (2019), it validates culturally relevant modules, ensuring edtech aligns with student values and improves outcomes.
Key Research Challenges
Capturing Subjective Experiences
Interviews uncover nuanced teacher challenges in e-learning, but coding biases distort themes (Lukas and Yunus, 2021). Standardized protocols are needed for reliability across studies. Small sample sizes limit generalizability in ESL contexts (Hong and Ganapathy, 2017).
Integrating Mixed Methods
Combining thematic analysis with fuzzy logic assessments struggles with data alignment (Amelia et al., 2019). Qualitative insights often conflict with quantitative metrics in project evaluations (Ayca and Karal, 2017). Frameworks for hybrid validation remain underdeveloped.
Evaluating Cultural Relevance
Assessing ethnoscience modules requires context-specific themes, but tools overlook local values (Ardianti et al., 2019). Teacher perceptions vary by philosophy alignment, complicating edtech adaptation (Lessani et al., 2014). Scaling qualitative findings across diverse regions is inconsistent.
Essential Papers
ESL Teachers’ Challenges in Implementing E-learning during COVID-19
Brenda Anak Lukas, Melor Md Yunus · 2021 · International Journal of Learning Teaching and Educational Research · 111 citations
Education sector in Malaysia had put emphasis on the use of online learning or e-learning with technology and devices as a mediator of communication to replace face-to-face learning during the COVI...
To Investigate ESL Students’ Instrumental and Integrative Motivation towards English Language Learning in a Chinese School in Penang: Case Study
Yee Chee Hong, Malini Ganapathy · 2017 · English Language Teaching · 75 citations
Malaysians have long realised the importance of being competent in English as one of the success factors in attaining their future goals. However, English is taught as a second language in Malaysia...
A Needs Assessment of Edutainment Module with Ethnoscience Approach Oriented to the Love of the Country
Sekar Dwi Ardianti, Savitri Wanabuliandari, Sigit Saptono et al. · 2019 · Jurnal Pendidikan IPA Indonesia · 57 citations
In this globalization era, young generations are having problems regarding the love of the country character. The purposes of this research were (1) analyzing students’ need on an entertaining modu...
An application of fuzzy analytic hierarchy process (FAHP) for evaluating students project
CEBİ Ayca, KARAL Hasan · 2017 · Educational Research and Reviews · 54 citations
In recent years, artificial intelligence applications for understanding the human thinking process and transferring it to virtual environments come into prominence. The fuzzy logic which paves the ...
Lack of Motivation Factors Creating Poor Academic Performance in the Context of Graduate English Department Students
Islahul Mauliya, Resty Zulema Relianisa, Umy Rokhyati · 2020 · Linguists Journal of Linguistics and Language Teaching · 52 citations
At the graduate level, students’ poor performance cuts across almost all the compulsory subjects in which English is inclusive. Poor academic performance of students is one problem impeding the smo...
Meta-analysis of Student Performance Assessment Using Fuzzy Logic
Nia Amelia, Ade Gafar Abdullah, Yadi Mulyadi · 2019 · Indonesian Journal of Science and Technology · 44 citations
The assessment system generally requires transparency and objectivity to assess student performance in terms of abstraction. Fuzzy logic method has been used as one of the best methods to reduce th...
Examining the teachers’ pedagogical knowledge and learning facilities towards teaching quality
Muhd Zulhilmi Haron, Mohd Muslim Md Zalli, Mohamad Khairi Othman et al. · 2021 · International Journal of Evaluation and Research in Education (IJERE) · 29 citations
<p>The purpose of this study was to examine the relationship between teachers’ pedagogical knowledge, learning facilities and the teaching quality of teachers in the Ulul Albab Tahfiz Model (...
Reading Guide
Foundational Papers
Start with Lessani et al. (2014) for teacher perceptions of educational philosophy in math edtech and Mansor (2007) on English-medium challenges, as they establish baseline qualitative evaluation needs.
Recent Advances
Prioritize Lukas and Yunus (2021) for COVID-19 e-learning themes and Rahman et al. (2022) on AI writing tools, capturing current user experience shifts.
Core Methods
Core techniques include thematic analysis of interviews (Lukas and Yunus, 2021), fuzzy AHP for assessments (Ayca and Karal, 2017), and needs analysis via surveys (Ardianti et al., 2019).
How PapersFlow Helps You Research Qualitative Data Analysis in Edtech Evaluation
Discover & Search
Research Agent uses searchPapers and exaSearch to find thematic analyses in edtech, such as 'ESL Teachers’ Challenges in Implementing E-learning during COVID-19' by Lukas and Yunus (2021), then citationGraph reveals 111-cited connections to motivation studies like Mauliya et al. (2020).
Analyze & Verify
Analysis Agent applies readPaperContent to extract interview themes from Chin et al. (2021), verifies response accuracy with CoVe chain-of-verification, and uses runPythonAnalysis for GRADE grading of evidence strength in fuzzy evaluations (Amelia et al., 2019). Statistical verification confirms theme frequencies across papers.
Synthesize & Write
Synthesis Agent detects gaps in qualitative edtech validation, flags contradictions between teacher challenges (Lukas and Yunus, 2021) and AWE perceptions (Rahman et al., 2022); Writing Agent employs latexEditText, latexSyncCitations, and latexCompile for mixed-methods reports with exportMermaid diagrams of thematic flows.
Use Cases
"Run statistical analysis on motivation themes from ESL edtech papers"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas theme frequency, matplotlib visualizations) → researcher gets CSV export of quantified qualitative data from Mauliya et al. (2020).
"Compile LaTeX report on e-learning teacher challenges"
Research Agent → citationGraph → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → researcher gets compiled PDF with citations from Lukas and Yunus (2021).
"Find code for fuzzy logic in edtech student assessment"
Research Agent → paperExtractUrls on Ayca and Karal (2017) → Code Discovery → paperFindGithubRepo → githubRepoInspect → researcher gets inspected repositories implementing FAHP for evaluations.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ edtech papers: searchPapers → citationGraph → DeepScan 7-step analysis with GRADE checkpoints on qualitative themes from Chin et al. (2021). Theorizer generates frameworks from interview data in Hong and Ganapathy (2017), chaining readPaperContent → gap detection → theory export. DeepScan verifies cultural needs assessments (Ardianti et al., 2019) via CoVe.
Frequently Asked Questions
What is Qualitative Data Analysis in Edtech Evaluation?
It involves thematic analysis of interviews and feedback to evaluate edtech user experiences and efficacy, as in teacher challenges during COVID-19 (Lukas and Yunus, 2021).
What methods are commonly used?
Thematic coding from interviews, case studies, and hybrid fuzzy logic integration appear in key works like Amelia et al. (2019) meta-analysis and Ayca and Karal (2017) FAHP applications.
What are key papers?
Top cited: Lukas and Yunus (2021, 111 citations) on e-learning challenges; Hong and Ganapathy (2017, 75 citations) on ESL motivation; Ardianti et al. (2019, 57 citations) on ethnoscience needs.
What open problems exist?
Scaling qualitative insights across cultures, integrating with AI tools like AWE (Rahman et al., 2022), and reducing biases in mixed-methods edtech validation remain unresolved.
Research Educational Methods and Technology with AI
PapersFlow provides specialized AI tools for Computer Science researchers. Here are the most relevant for this topic:
AI Literature Review
Automate paper discovery and synthesis across 474M+ papers
Code & Data Discovery
Find datasets, code repositories, and computational tools
Deep Research Reports
Multi-source evidence synthesis with counter-evidence
AI Academic Writing
Write research papers with AI assistance and LaTeX support
See how researchers in Computer Science & AI use PapersFlow
Field-specific workflows, example queries, and use cases.
Start Researching Qualitative Data Analysis in Edtech Evaluation with AI
Search 474M+ papers, run AI-powered literature reviews, and write with integrated citations — all in one workspace.
See how PapersFlow works for Computer Science researchers