Abstract
In the current context of social distancing due to the pandemic of COVID-19, teachers are willing to find how to animate their classes without being physically present. Active learning can guide cognitive, motivational, and emotional learning processes while giving learners control over their learning. Research has shown that active learning offers better outcomes than passive learning. Indeed, this learning technique can enhance motivation, self-regulation, as well as soft skills. However, there is a need to support students' self-regulation during active learning. Therefore, teachers can use techniques from technology-enhanced learning tools and online tools to design an active learning class. This chapter aims to study how digital learning tools can promote active e-learning and foster students' self-regulation and motivation. Further research directions are given, regarding neurolearning, the role of the body during a learning process, and neuroadaptive personalized learning environments.
TopIntroduction
The context of the Covid-19 epidemic has pushed the teaching world to change. Research has shown that learning situations that include some activities are more effective than passive learning situations for students (Lombardi et al., 2021). Researchers speak of learning-by-doing (Dewey, 1938). Active learning entails that students can take control of their learning process through levels of metacognitive sense-making, self-assessment, and reflection (Lombardi et al., 2021). Metacognition is now part of learning strategies that positively influence academic performance through the cognitive and affective regulatory processes (Veenman & Spaans, 2005). For example, self-regulation is one antecedent of student performance in face-to-face and e-learning environments (Delen & Liew, 2016). Unfortunately, not all students can be self-regulated during a learning experience (Moos & Bonde, 2016).
How can active learning models guide students through cognitive, motivational, and emotional learning processes in a distance education setting? Moreover, how can active learning models help students self-regulating their cognition, motivation, and emotional learning processes? Therefore, this chapter aims to study how learning technology tools can promote active learning via e-learning and reflect on how teachers and instructional designers can guide self-regulation and motivation thanks to active learning tools.
First, the background presents the current digital transition from face-to-face to e-learning classes, the theory about intelligence and learning, and the concepts of motivation and engagement. Second, the main focus of this chapter presents how adaptive learning can respond to students’ needs, active learning models from the literature, and different designing techniques. Third, solutions and recommendations highlight technology-enhanced active learning, the use of prompts, which can guide self-regulation, and online tools to favor interactivity, cooperation, and active learning. Finally, future research directions highlight the neurolearning theory, notably the role of the body during learning, and the neuroadaptive tools that can promote self-regulation.
Key Terms in this Chapter
Educative Chatbot: Thinking processors and mentors that impart knowledge to students, based on analyzing the learning patterns and comprehension and adapt to their pace through personalized messages.
Metacognition: Knowledge about one’s own knowledge and applying that knowledge in practice ( Flavell, 1979 ).
Prompt: Prompts are short hints or questions created to activate knowledge, strategies, or skills that students do not use spontaneously but already have ( Wirth, 2009 ).
Collaborative Learning: A relationship among learners that involves positive interaction and personal engagement (Hibbi et al., 2021 AU99: The in-text citation "Hibbi et al., 2021" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Self-Regulation: Processes that allow learners to control goal-directed activities over time and across changing circumstances ( Karoly, 1993 ). Self-regulated learners are students with meta-cognitive, motivational, and behavioral control during their learning process ( Zimmerman, 1989 ).
Interactive Response System (IRS): A learning-centered, interactive, teaching–learning technology.
Technology-Enhanced Active Learning (TEAL): The enhancement of the learning process through the use of technology ‘where technology plays a significant supportive role’ ( Goodyear & Retalis, 2010 ).
Intelligent Tutoring Systems (ITS): A digital learning system that monitors and adapts to a learner's behavior and knowledge state.
Active Learning: Anything course-related that all students in a class session are called upon to do other than simply watching, listening, and taking notes ( Felder & Brent, 2009 ).