首页|Researchers from North Carolina State University (NC State) Describe Findings in Machine Learning (Leveraging Student Planning In Game-based Learning Environmen ts for Self-regulated Learning Analytics)
Researchers from North Carolina State University (NC State) Describe Findings in Machine Learning (Leveraging Student Planning In Game-based Learning Environmen ts for Self-regulated Learning Analytics)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Investigators publish new report on Ma chine Learning. According to news reporting originating in Raleigh, North Caroli na, by NewsRx journalists, research stated, "The process of setting goals and cr eating plans is crucial for self-regulated learning (SRL), yet students often st ruggle to construct efficient plans and establish goals. Adaptive learning envir onments hold promise for assisting students with such processes through adaptive scaffolding." Financial support for this research came from National Science Foundation (NSF). The news reporters obtained a quote from the research from North Carolina State University (NC State), "Through the examination of data collected from 144 middl e school students, we present a datadriven analysis of students' explicit plann ing activities in Crystal Island, a narrative game-based learning environment. I n this game, students are provided with a planning support tool that aids them i n externalizing their science-related goals and plans before putting them into a ction. We extracted features from their planning tool use and connected them to several SRL processes and problem-solving outcomes. We found that students who e ngaged with the planning support tool were more likely to successfully complete the learning scenario. To investigate the potential for adaptive support with th is tool, we also constructed a student plan recognition framework aimed at predi cting students' goals and planned action sequences. This framework uses student gameplay sequences as input and student interactions with the planning tool as l abels for both prediction tasks. We evaluated these tasks using six machine lear ning models and found that all approaches improved on the majority baseline clas sification performance. We then investigated additional machine-learning archite ctures and a technique for detecting when students enact all steps in their plan s as methods for improving the framework. We demonstrated performance improvemen t with these enhancements."
RaleighNorth CarolinaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningNor th Carolina State University (NC State)
NETL
NSTL
