Editorial: Beyond Cognitive Ability

Srécko Joksimovíc, George Siemens, Yuan Wang, M. O. S. Pedro, Jason D. Way
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引用次数: 11

Abstract

The past 70 years of research in learning has primarily favoured a cognitive perspective. As such, learning and learning performance were measured based on factors such as memory, encoding, and retrieval. More sophisticated learning activities, such as perspective changes, still relied on a fundamental cognitive architecture (Dunlosky & Rawson, 2019). Early researchers advocating for a constructivist learning lens, such as Piaget, also assessed development on a range of cognitive tasks. Over the past several decades, this view of learning as cognitive has given rise to a range of augmenting perspectives. Researchers increasingly focus on mindsets, social learning, peer effects, self-regulation, and self-perception to evaluate the broader scope of learning. For learning analytics (LA), this transition has important implications for data collection and analysis, tools and technologies used, research design, and experimentation. This special issue continues existing conversations around LA and emerging competencies (Dawson & Siemens, 2014; Buckingham Shum & Crick, 2016) but also reflects the growing number of researchers engaging with these topics.
社论:超越认知能力
过去70年的学习研究主要倾向于认知视角。因此,学习和学习表现是基于记忆、编码和检索等因素来衡量的。更复杂的学习活动,如视角变化,仍然依赖于基本的认知架构(Dunlosky & Rawson, 2019)。提倡建构主义学习视角的早期研究人员,如皮亚杰,也评估了一系列认知任务的发展。在过去的几十年里,这种将学习视为认知的观点引发了一系列的扩展观点。研究人员越来越关注心态、社会学习、同伴效应、自我调节和自我感知来评估更广泛的学习范围。对于学习分析(LA)来说,这种转变对数据收集和分析、使用的工具和技术、研究设计和实验具有重要意义。本期特刊继续围绕洛杉矶和新兴能力展开现有对话(Dawson & Siemens, 2014;Buckingham Shum & Crick, 2016),但也反映了越来越多的研究人员参与这些主题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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