Indian practitioner survey links real-time analytics to stronger adaptive digital assessment practices

A cross-sectional survey of 476 Indian educators, instructional designers and administrators found that real-time analytics had the strongest statistical association with adaptive digital assessment practices, including adaptive intervention, formative feedback, inclusive assessment and self-regulated learning support. The study, published in Discover Education in 2026, used Partial Least Squares Structural Equation Modelling to assess a framework linking multimodal learning analytics infrastructure with those practices.

The researchers initially developed an 11-construct framework combining Universal Design for Learning principles with multimodal learning analytics infrastructure, then converted it into a 44-item survey. The infrastructure side comprised data quality, technology platforms and real-time analytics, while the practice outcomes comprised adaptive intervention, formative feedback, inclusive assessment and self-regulated learning support. Experts reviewed the instrument for clarity, relevance and construct alignment, and 25 educators and instructional designers completed a pilot test.

Participants were recruited through 12 universities and 15 K-12 school networks across Kerala, Karnataka, Tamil Nadu, Odisha and Delhi, with additional snowball referrals. Of 687 people contacted, 476 completed the survey, a response rate of 69.3%. Teachers and educators made up 59.9% of the final sample, instructional designers 26.7% and administrators 13.4%. K-12 participants represented 41.6% of respondents and higher-education participants 58.4%. Data were collected from October 2025 to February 2026.

All measures used five-point Likert responses, and the researchers chose PLS-SEM because the analysis prioritised prediction and explained variance, included reflective and formative measurement structures and used practitioner survey data that did not require multivariate normality. Statistical significance was evaluated with 5,000 bootstrap resamples, with path coefficients considered significant at p<.05. The final instrument contained 24 items measuring seven constructs, and all constructs had composite reliability above 0.70.

Nine of the 12 directional hypotheses were statistically significant. Real-time analytics had the largest standardised path coefficient, linking it to adaptive intervention at β=0.546 with p<.001 and a large effect size of f²=0.421. The three infrastructure constructs together explained 46.0% of the variance in adaptive intervention, while explained variance for the other outcomes ranged from 19.4% to 35.4%. All four outcomes had positive predictive relevance, although the model’s explanatory power was bounded by factors that were not included.

Technology platforms showed significant positive associations with all four outcomes, with their strongest relationship being with self-regulated learning support at β=0.323. By contrast, data quality had a limited direct association, reaching statistical significance only with adaptive intervention at β=0.110 and p=.016. The authors interpreted this pattern as consistent with data functioning as an enabling resource whose practical value depends on technology and analytics that can make information accessible and actionable.

The Universal Design for Learning measures did not survive validation and were removed from the final model. Universal Design for Learning Action and Expression and Engagement were empirically indistinguishable, with an HTMT ratio of 1.009, and merging them produced an average variance extracted of 0.313. Personalised Feedback and Formative Assessment were also merged because their HTMT was 0.962, while 20 items with loadings below 0.60 were removed. The authors said these results indicate measurement problems in practitioner self-reports, not that Universal Design for Learning lacks value; possible explanations include holistic practitioner understanding, inadequate survey measures, contextual variation and insufficient professional development.

Several limitations constrain the findings. The cross-sectional, self-report design does not support causal inference and may be affected by common-method and social-desirability bias. Two constructs were measured with only two items each and had low Cronbach’s alpha, while two others met the primary convergent-validity threshold only through a secondary exception. Discriminant validity was not formally established between real-time analytics and adaptive intervention: their HTMT was 0.901, and the 95% bootstrap confidence interval of 0.808 to 0.987 included both commonly used thresholds. The model fit was also mixed, with SRMR at 0.094 and NFI at 0.528.

Purposive recruitment in one country may have overrepresented technologically proficient or enthusiastic practitioners, limiting generalisability beyond the sampled Indian contexts. The model did not test mediation or moderation, distinguish platform- or vendor-specific differences, or include potential influences such as institutional support, teacher artificial-intelligence literacy, assessment culture, professional development, leadership and student characteristics. The validated 24-item instrument can nevertheless support benchmarking of reported multimodal learning analytics implementation, while the observed results indicate that real-time analytics deserve consideration alongside—not instead of—data quality, technology platforms, governance, staff training and technical support.

Subject of Research: Cross-sectional validation of multimodal learning analytics infrastructure and adaptive digital assessment practices among 476 Indian education practitioners.

Article Title: Multimodal learning analytics infrastructure and adaptive digital assessment

Article References: Asha, S., & Joshith, V. P. (2026). Multimodal learning analytics infrastructure and adaptive digital assessment. Discover Education, 5(1), Article 1193. https://doi.org/10.1007/s44217-026-02301-3

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02301-3

Keywords: multimodal learning analytics, adaptive assessment, real-time analytics, learning analytics, PLS-SEM, digital assessment, formative feedback, self-regulated learning, India, survey research, Multimodal, learning