摘要
作为学习分析领域的重要内容,学习投入的评测日益成为研究热点。对学习投入的概念与特征的阐释,反映出当前对于经典学习分析的局限,即“路灯效应”(Streetlight Effect)。其有可能使研究者偏离解决“真实场景”中的问题,而多模态数据支持的学习评测,恰恰契合了学习投入的动态、多维、境脉化的特征。多模态的数据获取,可以从交互情景中的行为分析、单模态传感器与多模态传感器三个维度来分类。多模态数据经过建模场景、数据源与精度等方面的刻画,可实现对学习者交互状态、辍学率、心智游移水平、注意力以及成功表现等指标的评估,体现出对复杂认知能力衡量、改善建模精度以及对数据集整体意义还原的实践价值。未来,对学习投入的评测研究,应强化对理论模型的构建,充分借助脑科学、教育神经科学等的技术手段,阐释学习者外部行为表现、认知过程与内部生理的相关机制,构建科学的生物数据库以及对脱离投入提供更为有效的解释与干预,从而为智能时代的个性化学习提供“增值”。
As the important content in learning analytics,the evaluation of learning engagement has become a research focus.To this end,the concepts and characteristics of learning engagement are explained,and the limitations of classical learning analytics are pointed out.That is,the“streetlight effect”may lead researchers to deviate from solving problems in“real scenarios”,and the learning evaluation supported by multimodal data fits the dynamic,multi-dimensional,and contextual characteristics of learning engagement.Multimodal data acquisition can be classified from three dimensions:behavior analysis in interactive scenarios,single-modal sensors and multimodal sensors.Multimodal data,characterized by modeling scenarios,data sources,and accuracy,can realize the assessment of learner interaction status,drop-out rate,mental migration level,attention and success performance,etc.Besides,it reflects practical value to complex cognitive abilities in measuring and improving modeling accuracy and restoring the overall meaning of the data set.The evaluation study of future learning engagement should strengthen the construction of theoretical models,fully utilizing the technical methods of brain science and educational neuroscience to explain the learners’external behavior performance,cognitive processes and internal physiological related mechanisms.Meanwhile,it should build scientific biological databases and offer more effective explanations and interventions for engagement detachment,thus providing“added value”for personalized learning in the intelligent age.
作者
张琪
武法提
许文静
Zhang Qi;Wu Fati;Xu Wenjing(School of Education,Huaibei Normal University,Huaibei Anhui 235000;College of Education Technology,Beijing Normal University,Beijing 100875)
出处
《远程教育杂志》
CSSCI
北大核心
2020年第1期76-86,共11页
Journal of Distance Education
基金
教育部人文社会科学青年基金项目“全息数据支持的学习投入建模与干预研究”（项目编号：18YJC880126）的研究成果
关键词
多模态
数据建模
学习投入
智能评价
研究趋向
学习分析
情感分析
Multimodality
Data Modeling
Learning Engagement
Intelligent Evaluation
Research Trends
Learning Analytics
Sentiment Analysis
