Shallow strategy development in a teachable agent environment designed to support self-regulated learning

Roscoe, Rod D.; Segedy, James R.; Sulcer, Brian; Jeong, Hogyeong; Biswas, Gautam

doi:10.1016/j.compedu.2012.11.008

Cited by 43 publications

(26 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As discussed earlier, four lines of research have been conducted within the learning by teaching paradigm, including learning by preparing to teach (e.g., Annis, 1983;Bargh & Schul, 1980;Fiorella & Mayer, 2013;Renkl, 1995), learning by teaching (Annis, 1983;Fiorella & Mayer, 2013, 2014, learning through peer tutoring (Chi et al, 2001;Roscoe, 2014;Roscoe & Chi, 2007), and teachable agents (Biswas et al, 2005(Biswas et al, , 2010Roscoe et al, 2013). Although this study only focused on learning by preparing to teach, other studies in the learning by teaching literature have systematically explored the effects of learning by preparing to teach versus actually teaching (Annis, 1983;Fiorella & Mayer, 2013, 2014.…”

Section: Recommendations For Future Researchmentioning

confidence: 99%

“…Others may include peers or computerbased agents (Biswas et al, 2010;Roscoe & Chi, 2007). Four lines of research have been conducted within the learning by teaching paradigm, including learning by preparing to teach (Annis, 1983;Bargh & Schul, 1980;Fiorella & Mayer, 2013;Renkl, 1995), learning by (actually) teaching (Annis, 1983;Fiorella & Mayer, 2013, 2014, learning through peer tutoring (Chi, Siler, Jeong, Yamauchi, & Hausmann, 2001;De Backer, Van Keer, & Valcke, 2012;King, Staffieri, & Adelgais, 1998;Roscoe & Chi, 2007), and teachable agents, such as computer-based agents (Biswas et al, 2005(Biswas et al, , 2010Roscoe, Segedy, Sulcer, Jeong, & Biswas, 2013).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Learning by preparing to teach: Fostering self-regulatory processes and achievement during complex mathematics problem solving.

Muis

Psaradellis

Chevrier

et al. 2016

Journal of Educational Psychology

View full text Add to dashboard Cite

We developed an intervention based on the learning by teaching paradigm to foster self-regulatory processes and better learning outcomes during complex mathematics problem solving in a technologyrich learning environment. Seventy-eight elementary students were randomly assigned to 1 of 2 conditions: learning by preparing to teach, or learning for learning (control condition). Students' conceptualizations (task definitions) of the problem, self-regulatory processes, and mathematics achievement were then compared across the 2 conditions. To measure task definitions of the mathematics problem, students developed concept maps of the problem using a tablet application. To capture self-regulatory processes, students were asked to think out loud as they solved the problem. Results revealed that students in the learning by preparing to teach intervention developed a more detailed and better-organized concept map of the problem compared with students in the control condition. Students in the learning by preparing to teach intervention also engaged in more metacognitive processing strategies and had higher levels of mathematics problem solving achievement compared with students in the control condition. No differences were found, however, in planning and goal setting or in use of cognitive strategies across the 2 conditions. Implications of this research suggest students' initial task definitions may be a key factor in differences found when learning by teaching compared with solely learning for learning.

show abstract

Section: Recommendations For Future Researchmentioning

confidence: 99%

mentioning

confidence: 99%

Learning by preparing to teach: Fostering self-regulatory processes and achievement during complex mathematics problem solving.

Muis

Psaradellis

Chevrier

et al. 2016

Journal of Educational Psychology

View full text Add to dashboard Cite

show abstract

“…One representative example is Betty's Brain (Roscoe, Segedy, Sulcer, Jeong, & Biswas, 2013;Segedy, Biswas, & Sulcer, 2014). One representative example is Betty's Brain (Roscoe, Segedy, Sulcer, Jeong, & Biswas, 2013;Segedy, Biswas, & Sulcer, 2014).…”

Section: Agentsmentioning

confidence: 99%

A framework for self‐regulated digital learning (SRDL)

Yen

Chen

Wang

et al. 2018

Computer Assisted Learning

View full text Add to dashboard Cite

This article develops a framework for self‐regulated digital learning, which supports for self‐regulated learning (SRL) in e‐learning systems. The framework emphasizes 8 features: learning plan, records/e‐portfolio and sharing, evaluation, human feedback, machine feedback, visualization of goals/procedures/concepts, scaffolding, and agents. Each feature facilitates or supports one or more SRL skills, including planning, monitoring and evaluating learning, applying appropriate cognitive strategies, and setting standards of products or performance. The implementation in domain‐general and ‐specific systems as illustrated by web‐based inquiry and problem‐solving are discussed. Examples and learning effects are elicited from the literature to demonstrate various designs. Approaches for designing SRL systems, educational implications, and new directions for future research incorporating SRL into digital learning are presented.

show abstract

“…Students must be able to convert causal information between and among the system's hypertext resources, their internal knowledge structures, and the causal map representation. Students unfamiliar with causal structures or how to represent knowledge using them will most likely struggle to succeed in completing the Betty's Brain learning task (Segedy, Kinnebrew, & Biswas, 2013;Roscoe et al, 2013).…”

Section: Cognitive and Metacognitive Problem-solving Processes In Oelesmentioning

confidence: 99%

Using Coherence Analysis to Characterize Self-Regulated Learning Behaviours in Open-Ended Learning Environments

2015

Self Cite

View full text Add to dashboard Cite

ABSTRACT:Researchers have long recognized the potential benefits of open-ended computerbased learning environments (OELEs) to help students develop self-regulated learning (SRL) behaviours. However, measuring self-regulation in these environments is a difficult task. In this paper, we present our work in developing and evaluating coherence analysis (CA), a novel approach to interpreting students' learning behaviours in OELEs. CA focuses on the learner's ability to seek out, interpret, and apply information encountered while working in the OELE. By characterizing behaviours in this manner, CA provides insight into students' open-ended problem-solving strategies as well as the extent to which they understand the nuances of their current learning task. To validate our approach, we applied CA to data from a recent classroom study with Betty's Brain. Results demonstrated relationships between CA-derived metrics, prior skill levels, task performance, and learning. Taken together, these results provide insight into students' SRL processes and suggest targets for adaptive scaffolds to support students' development of science understanding and open-ended problem-solving skills.

show abstract

Shallow strategy development in a teachable agent environment designed to support self-regulated learning

Cited by 43 publications

References 29 publications

Learning by preparing to teach: Fostering self-regulatory processes and achievement during complex mathematics problem solving.

Learning by preparing to teach: Fostering self-regulatory processes and achievement during complex mathematics problem solving.

A framework for self‐regulated digital learning (SRDL)

Using Coherence Analysis to Characterize Self-Regulated Learning Behaviours in Open-Ended Learning Environments

Contact Info

Product

Resources

About