Search for: All records

Researchers and educators have developed a variety of computer-based technologies intended to facilitate self-regulated learning (SRL), which refers to iterative learning processes wherein individuals set plans and goals, complete tasks, monitor their progress and outcomes, and adapt future efforts. This paper draws upon the SRL literature and related work to articulate two fundamental principles for designing SRL-promoting technologies: the Platform Principle and the Support Principle. The Platform Principle states that SRL-promoting technologies must incorporate clear platforms (i.e., tools and features) for engaging in planning, enacting, monitoring, and adapting. The Support Principle states that SRL-promoting technologies must include clear scaffolds for strategies, metacognition, motivation, and independence. These principles can be applied heuristically to formatively assess how and whether given learning technologies enable and scaffold self-regulation. More broadly, these assessments can empower educational technology creators and users to strategically design, communicate, and study technologies aligned with self-regulation. An exemplar application of the framework is presented using the PERvasive Learning System (PERLS) mobile SRL technology. 

The current study an empirical evaluation of the PERvasive Learning System (PERLS). PERLS is a mobile microlearning platform designed for learning anytime and anywhere, taking advantage of planned and unplanned time during a learner’s daily schedule to enhance and reinforce learning. Soldiers taking classes from the Sabalauski Air Assault School at Fort Campbell, KY were recruited. This evaluation compared the impact of PERLS on soldiers’ self-efficacy for their self-regulated learning ability. This evaluation found evidence of impact for the PERLS when implemented into classroom setting with soldiers that used PERLS indicating higher self-efficacy scores. 

Virtual reality (VR) has a high potential to facilitate education. However, the design of many VR learning applications was criticized for lacking the guidance of explicit and appropriate learning theories. To advance the use of VR in effective instruction, this study proposed a model that extended the cognitive-affective theory of learning with media (CATLM) into a VR learning context and evaluated this model using a structural equation modeling (SEM) approach. Undergraduate students ( n = 77) learned about the solar system in a VR environment over three sessions. Overall, the results supported the core principles and assumptions of CATLM in a VR context (CATLM-VR). In addition, the CATLM-VR model illustrated how immersive VR may impact learning. Specifically, immersion had an overall positive impact on user experience and motivation. However, the impact of immersion on cognitive load was uncertain, and that uncertainty made the final learning outcomes less predictable. Enhancing students’ motivation and cognitive engagement may more directly increase learning achievement than increasing the level of immersion and may be more universally applicable in VR instruction. 

Detection and responding to a player’s affect are important for serious games. A method for this purpose was tested within Chem-o-crypt, a game that teaches chemical equation balancing. The game automatically detects boredom, flow, and frustration using the Affdex SDK from Affectiva. The sensed affective state is then used to adapt the game play in an attempt to engage the player in the game. A randomized controlled experiment incorporating a Dynamic Bayesian Network that compared results from groups with the affect-sensitive states vs those without revealed that measuring affect and adapting the game improved learning for low domain-knowledge participants. 

Content Agnostic Game Engineering (CAGE) architecture utilizes content agnostic mechanics to create educational games that can teach multiple contents. However, the player engagement goes down when second content is played using the same game mechanics. A content agnostic stealth assessment can aid a CAGE game in sustaining the engagement level of its players. A potentially generalizable method for this was tested using Chem-o-crypt, a CAGE game that can teach chemistry and cryptography contents. The game automatically detects frustration, flow, and boredom using the Affdex SDK from Affectiva. A randomized controlled experiment incorporating real-time game adaptation revealed that using stealth assessment can help sustain engagement in a CAGE game when playing multiple contents. 

A learning strategy analysis was performed on the Emotive Virtual Patient System, an augmented reality platform that teaches medical students doctor-patient communication skills. The Emotive Virtual Patient System is a complex mixed reality platform that includes both virtual and human peers/instructors who use natural language processing to provide feedback and dialog modeling as a means to improve patient communication learning outcomes. The learning strategy analysis (i.e., system learning strategy/component review, literature review, and heuristic evaluation of best practices) was conducted on the early system plans to determine its potential in supporting student learning and to provide shortand-long-term design considerations. The analysis identified three major categories for potential consideration: verbal interactions, user groups/system objective monitoring, and security. Specific recommendations were given for each of these areas, as supported by the literature.