Triggering, developing and internalising teamworking skills in neuro-typical and neuro-atypical students with a computer orchestrated group learning environment: a multi case study

Towards a new future in engineering education, new scenarios that european alliances of tech universities open up(2022)

引用 0|浏览0
暂无评分
摘要
Project-based learning and flipped classroom approaches are often used for developing team working skills in graduates. However, many engineering schools face efficiency and effectiveness challenges when it comes to facilitating students in these settings. For neuro-atypical (NAT) students, such as those with Attention Deficit Hyperactivity Disorder (ADHD) or Autism, support for developing teamworking skills can be limited. Even neuro-typical (NT) students find teamwork challenging and can benefit from an intervention that supports development of such skills. Self, Co and Shared regulation skills are considered important for effective team working. Regulation is a multi-staged process, which includes goal setting, planning, doing, monitoring and evaluating own and a team's work. Research on use of computer scripts to successfully orchestrate the multiple stages at a shared level shows only partial success. Many Computer Supported and Collaborative Learning studies cite over-scripting as a common criticism related to orchestration of shared regulation and team work. This work investigates "How computer orchestration scripts affect the triggering and internalisation of Self, Co and Social regulation skills in NT and NAT students when using a Computer Orchestrated Group Learning Environment (COGLE)?". COGLE was used with first year neurotypical and neuro-atypical engineering students to study its impact on triggering existing and/or internalising new regulation scripts in team working. Qualitative data from two literal replication cases were analysed. This work shows how different types of scripts in COGLE helped trigger, develop and internalise regulation skills and highlights areas where more work is needed.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要