Digital-technology courses now cover subject matter that resists plain-text explanation: cloud architecture, blockchain mechanics, algorithmic platforms, artificial intelligence. Teaching this material well usually means combining prose with statistics and visuals, yet most instruction still relies on text alone. The international literature on multimodal learning analytics has built solid frameworks for classifying and combining heterogeneous educational data (Mu, Cui, & Huang, 2020), and digital-competency instruments have been validated across several institutional settings (Moreira-Choez et al., 2024).                   

What has not been done is turning these ideas into a tested teaching method for digital-technology instruction specifically, or checking whether such a method holds up in a Central Asian university. This study builds and tests a multitype data-fusion teaching method grounded in TPACK (Mishra & Koehler, 2006), connectivism (Siemens, 2005), and the zone of proximal development (Vygotsky, 1978). Sixty-four students at a Uzbekistani technical university were split into a text-only control group and a multitype-fusion experimental group across eleven course topics. The experimental group scored 13.4 points higher on average (Cohen’s d = 1.54, p < .001), needed 22% less time to master each topic, and showed a clear pattern: achievement rose with each added data type, from r = 0.42 for text plus table up to r = 0.67 for text, table, and visual combined.   

Expert raters (Cronbach’s α = 0.89) and student feedback backed these numbers, though some students flagged sections as too dense, which points to a real limit on how much can be fused at once. Multitype fusion pedagogy works, in other words, but it needs calibration. The paper closes with concrete recommendations for curriculum designers and institutional policy. Expert raters (Cronbach’s α = 0.89) and student feedback backed these numbers, though some students flagged sections as too dense, which points to a real limit on how much can be fused at once. Multitype fusion pedagogy works, in other words, but it needs calibration. The paper closes with concrete recommendations for curriculum designers and institutional policy.

multitype data fusion; digital technology education; pedagogical methodology; multimodal learning analytics; TPACK; higher education; instructional design; digital competency; Uzbekistan; quasi-experimental study.

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Alimardonov, ..., Abdullayev, ..., & Rizayeva, ... Raqamli texnologiyalardan ta’lim tizimida foydalanish masalalari. TSUE ilmiy maqolalar to‘plami.

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