Matching Items (2)
Filtering by
- Creators: Connor, Carol M.
Description
Described is a study investigating the feasibility and predictive value of the Teacher Feedback Coding System, a novel observational measure of teachers’ feedback provided to students in third grade classrooms. This measure assessed individual feedback events across three domains: feedback type, level of specificity and affect of the teacher. Exploratory and confirmatory factor analysis revealed five factors indicating separate types of feedback: positive and negative academic-informative feedback, positive and negative behavioral-informative feedback, and an overall factor representing supportive feedback. Multilevel models revealed direct relations between teachers’ negative academic-informative feedback and students’ spring math achievement, as well as between teachers’ negative behavioral-informative feedback and students’ behavior patterns. Additionally, a fall math-by-feedback interaction was detected in the case of teachers’ positive academic-informative feedback; students who began the year struggling in math benefitted from more of this type of feedback. Finally, teachers’ feedback was investigated as a potential mediator in a previously established relation between teachers’ self-reported depressive symptoms and the observed quality of the classroom environment. Partial mediation was detected in the case of teachers’ positive academic-informative feedback, such that this type of feedback was accountable for a portion of the variance observed in the relation between teachers’ depressive symptoms and the quality of the classroom environment.
ContributorsMcLean, Leigh Ellen (Author) / Connor, Carol M. (Thesis advisor) / Lemery, Kathryn (Committee member) / Doane, Leah (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2015
Description
Concept maps are commonly used knowledge visualization tools and have been shown to have a positive impact on learning. The main drawbacks of concept mapping are the requirement of training, and lack of feedback support. Thus, prior research has attempted to provide support and feedback in concept mapping, such as by developing computer-based concept mapping tools, offering starting templates and navigational supports, as well as providing automated feedback. Although these approaches have achieved promising results, there are still challenges that remain to be solved. For example, there is a need to create a concept mapping system that reduces the extraneous effort of editing a concept map while encouraging more cognitively beneficial behaviors. Also, there is little understanding of the cognitive process during concept mapping. What’s more, current feedback mechanisms in concept mapping only focus on the outcome of the map, instead of the learning process.
This thesis work strives to solve the fundamental research question: How to leverage computer technologies to intelligently support concept mapping to promote meaningful learning? To approach this research question, I first present an intelligent concept mapping system, MindDot, that supports concept mapping via innovative integration of two features, hyperlink navigation, and expert template. The system reduces the effort of creating and modifying concept maps while encouraging beneficial activities such as comparing related concepts and establishing relationships among them. I then present the comparative strategy metric that modes student learning by evaluating behavioral patterns and learning strategies. Lastly, I develop an adaptive feedback system that provides immediate diagnostic feedback in response to both the key learning behaviors during concept mapping and the correctness and completeness of the created maps.
Empirical evaluations indicated that the integrated navigational and template support in MindDot fostered effective learning behaviors and facilitating learning achievements. The comparative strategy model was shown to be highly representative of learning characteristics such as motivation, engagement, misconceptions, and predicted learning results. The feedback tutor also demonstrated positive impacts on supporting learning and assisting the development of effective learning strategies that prepare learners for future learning. This dissertation contributes to the field of supporting concept mapping with designs of technological affordances, a process-based student model, an adaptive feedback tutor, empirical evaluations of these proposed innovations, and implications for future support in concept mapping.
This thesis work strives to solve the fundamental research question: How to leverage computer technologies to intelligently support concept mapping to promote meaningful learning? To approach this research question, I first present an intelligent concept mapping system, MindDot, that supports concept mapping via innovative integration of two features, hyperlink navigation, and expert template. The system reduces the effort of creating and modifying concept maps while encouraging beneficial activities such as comparing related concepts and establishing relationships among them. I then present the comparative strategy metric that modes student learning by evaluating behavioral patterns and learning strategies. Lastly, I develop an adaptive feedback system that provides immediate diagnostic feedback in response to both the key learning behaviors during concept mapping and the correctness and completeness of the created maps.
Empirical evaluations indicated that the integrated navigational and template support in MindDot fostered effective learning behaviors and facilitating learning achievements. The comparative strategy model was shown to be highly representative of learning characteristics such as motivation, engagement, misconceptions, and predicted learning results. The feedback tutor also demonstrated positive impacts on supporting learning and assisting the development of effective learning strategies that prepare learners for future learning. This dissertation contributes to the field of supporting concept mapping with designs of technological affordances, a process-based student model, an adaptive feedback tutor, empirical evaluations of these proposed innovations, and implications for future support in concept mapping.
ContributorsWang, Shang (Author) / Walker, Erin (Thesis advisor) / VanLehn, Kurt (Committee member) / Hsiao, Sharon (Committee member) / Long, Yanjin (Committee member) / Arizona State University (Publisher)
Created2019