%0 Book Section %1 Gabriel2022 %A Gabriel, Florence %A Cloude, Elizabeth B. %A Azevedo, Roger %B Social and Emotional Learning and Complex Skills Assessment: An Inclusive Learning Analytics Perspective %C Cham %D 2022 %E Wang, Yuan 'Elle' %E Joksimović, Srećko %E San Pedro, Maria Ofelia Z. %E Way, Jason D. %E Whitmer, John %I Springer International Publishing %K affect learninganalytics learningtechnologies measurement motivation self-regulatedlearning tracedata %P 93--108 %R 10.1007/978-3-031-06333-6_6 %T Using Learning Analytics to Measure Motivational and Affective Processes During Self-Regulated Learning with Advanced Learning Technologies %U https://doi.org/10.1007/978-3-031-06333-6_6 %X Self-regulated learning is an important predictor of students' academic achievement, employability, and career progression. Cognitive, affective, metacognitive, and motivational processes play a crucial role in students' ability to effectively monitor and regulate their learning while using advanced learning technologies (ALTs). This chapter focuses primarily on motivational and affective processes related to self-regulated learning with different types of ALTs such as serious games, intelligent tutoring systems, simulations and immersive technologies. While initial approaches to measuring students' motivation and affect have been predominantly centered around self-reported instruments, recent advances in learning analytics and educational data mining show significant benefits in using multimodal data as they reveal the dynamics of learning processes as they unfold with ALTs. As such, our chapter focuses on the use of novel techniques aimed at detecting, tracking, modeling, and fostering students' motivational and affective processes during learning, problem solving, and reasoning with various ALTs. We discuss implications for measuring motivational and affective processes using multimodal data for researchers, students, and educators by combining both objective and subjective methodologies. %@ 978-3-031-06333-6

@inbook{Gabriel2022, abstract = {Self-regulated learning is an important predictor of students' academic achievement, employability, and career progression. Cognitive, affective, metacognitive, and motivational processes play a crucial role in students' ability to effectively monitor and regulate their learning while using advanced learning technologies (ALTs). This chapter focuses primarily on motivational and affective processes related to self-regulated learning with different types of ALTs such as serious games, intelligent tutoring systems, simulations and immersive technologies. While initial approaches to measuring students' motivation and affect have been predominantly centered around self-reported instruments, recent advances in learning analytics and educational data mining show significant benefits in using multimodal data as they reveal the dynamics of learning processes as they unfold with ALTs. As such, our chapter focuses on the use of novel techniques aimed at detecting, tracking, modeling, and fostering students' motivational and affective processes during learning, problem solving, and reasoning with various ALTs. We discuss implications for measuring motivational and affective processes using multimodal data for researchers, students, and educators by combining both objective and subjective methodologies.}, added-at = {2022-08-28T11:46:26.000+0200}, address = {Cham}, author = {Gabriel, Florence and Cloude, Elizabeth B. and Azevedo, Roger}, biburl = {https://www.bibsonomy.org/bibtex/2925ac35072f5e4e27985d1395a34e6f5/ereidt}, booktitle = {Social and Emotional Learning and Complex Skills Assessment: An Inclusive Learning Analytics Perspective}, doi = {10.1007/978-3-031-06333-6_6}, editor = {Wang, Yuan 'Elle' and Joksimovi{\'{c}}, Sre{\'{c}}ko and San Pedro, Maria Ofelia Z. and Way, Jason D. and Whitmer, John}, interhash = {0003867a732d720caf076edb91642b26}, intrahash = {925ac35072f5e4e27985d1395a34e6f5}, isbn = {978-3-031-06333-6}, keywords = {affect learninganalytics learningtechnologies measurement motivation self-regulatedlearning tracedata}, pages = {93--108}, publisher = {Springer International Publishing}, timestamp = {2022-08-28T11:46:26.000+0200}, title = {Using Learning Analytics to Measure Motivational and Affective Processes During Self-Regulated Learning with Advanced Learning Technologies}, url = {https://doi.org/10.1007/978-3-031-06333-6_6}, year = 2022 }