Multimodal learning analytics for assessing teachers' self-regulated learning in planning technology-integrated lessons in a computer-based environment

Teachers' self-regulated learning (SRL) plays a crucial role in developing technological pedagogical content knowledge (TPACK), a complex professional skill. It is crucial to identify teachers' SRL activities that may lead to favorable TPACK. Previous studies have focused on the analysis of individual data sources from self-reported surveys or log files, which are insufficient to capture all SRL activities in the TPACK context. While multimodal learning analytics (MMLA) has the potential to improve SRL measurement, it remains unknown how multimodal data collected from different sources can be combined to identify salient features of SRL activities and examine how TPACK outcomes can be predicted by SRL activities identified from multimodal data. This study combined multimodal data from computer logs and think-aloud data to analyze teachers' SRL activities in designing a technology-integrated lesson. We identified the salient features of SRL from the combined data and explored how identified SRL activities might predict TPACK outcomes reflected in teacher-generated lesson plans. The results of random forest regression analysis show that three SRL activities from the logs and two from the think-aloud data formed the best combination that explained a significant proportion of variances in TPACK performance. The impact of MMLA in SRL measurement and the implication of this study are discussed.