Sidney K. D’Mello, Nicholas Duran, Amanda Michaels, Angela E. B. Stewart
{"title":"Improving collaborative problem-solving skills via automated feedback and scaffolding: a quasi-experimental study with CPSCoach 2.0","authors":"Sidney K. D’Mello, Nicholas Duran, Amanda Michaels, Angela E. B. Stewart","doi":"10.1007/s11257-023-09387-6","DOIUrl":null,"url":null,"abstract":"<p>We present CPSCoach 2.0, an automated system that provides feedback, instructional scaffolding, and practice to help individuals improve three collaborative problem-solving (CPS) skills drawn from a theoretical CPS framework: construction of shared knowledge, negotiation/coordination, and maintaining team function. CPSCoach 2.0 was developed and tested in the context of computer-mediated collaboration (video conferencing) with an educational game. It automatically analyzes users’ speech during a round of collaborative gameplay to provide personalized feedback and to select a target CPS skill for improvement. After multiple cycles of iterative testing and refinement, we tested CPSCoach 2.0 in a user study where 21 dyads (<i>n</i> = 42) completed four rounds of feedback and scaffolding embedded within five rounds of game-play in a single session. Using a quasi-experimental matching procedure, we found that the use of CPSCoach 2.0 was associated with improvement in CPS skill development compared to matched controls. Further, users found the automated feedback to be moderately accurate and had positive perceptions of the system, and these impressions were stronger for those who received higher scores overall. Results demonstrate the use of automated feedback and instructional scaffolds to support the development of CPS skills.</p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"1 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"User Modeling and User-Adapted Interaction","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11257-023-09387-6","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
We present CPSCoach 2.0, an automated system that provides feedback, instructional scaffolding, and practice to help individuals improve three collaborative problem-solving (CPS) skills drawn from a theoretical CPS framework: construction of shared knowledge, negotiation/coordination, and maintaining team function. CPSCoach 2.0 was developed and tested in the context of computer-mediated collaboration (video conferencing) with an educational game. It automatically analyzes users’ speech during a round of collaborative gameplay to provide personalized feedback and to select a target CPS skill for improvement. After multiple cycles of iterative testing and refinement, we tested CPSCoach 2.0 in a user study where 21 dyads (n = 42) completed four rounds of feedback and scaffolding embedded within five rounds of game-play in a single session. Using a quasi-experimental matching procedure, we found that the use of CPSCoach 2.0 was associated with improvement in CPS skill development compared to matched controls. Further, users found the automated feedback to be moderately accurate and had positive perceptions of the system, and these impressions were stronger for those who received higher scores overall. Results demonstrate the use of automated feedback and instructional scaffolds to support the development of CPS skills.
期刊介绍:
User Modeling and User-Adapted Interaction provides an interdisciplinary forum for the dissemination of novel and significant original research results about interactive computer systems that can adapt themselves to their users, and on the design, use, and evaluation of user models for adaptation. The journal publishes high-quality original papers from, e.g., the following areas: acquisition and formal representation of user models; conceptual models and user stereotypes for personalization; student modeling and adaptive learning; models of groups of users; user model driven personalised information discovery and retrieval; recommender systems; adaptive user interfaces and agents; adaptation for accessibility and inclusion; generic user modeling systems and tools; interoperability of user models; personalization in areas such as; affective computing; ubiquitous and mobile computing; language based interactions; multi-modal interactions; virtual and augmented reality; social media and the Web; human-robot interaction; behaviour change interventions; personalized applications in specific domains; privacy, accountability, and security of information for personalization; responsible adaptation: fairness, accountability, explainability, transparency and control; methods for the design and evaluation of user models and adaptive systems