混合课堂学生认知投入的多模态量化机制与干预

doi:10.3724/SP.J.1042.2024.01578

摘要/Abstract

摘要： 认知投入是指学习者的心理努力程度和认知策略应用程度, 是影响混合式教学中学习效果的关键因素, 但以往对学生认知投入的评估存在主观性强、模态单一等问题, 难以明晰混合课堂学生的认知投入水平, 制约混合课堂教学效果的改善。本研究拟结合数学建模、实验室实验与教学追踪准实验探究混合课堂认知投入的量化及干预。本研究将建立混合课堂认知投入的多模态量化表征模型以及认知投入提升策略, 形成面向高等教育的混合式教学方案并研制认知投入识别与干预的教研一体化工具, 为实现混合式教学提质增效提供理论与实践支持。

关键词: 混合课堂, 认知投入, 多模态测量指标, 干预策略

Abstract: Blended teaching, a new teaching model that integrates network technology with traditional face-to-face teaching, has gradually developed into a new typical teaching method in higher education. However, the complexity of elements in the blended teaching environment and the diversity of the learning environment have created significant challenges for improving blended teaching. Cognitive engagement refers to learners’ mental efforts and the degree to which cognitive strategies are applied, which is the critical factor affecting learning performance in blended teaching. Improving students’ cognitive engagement is essential for advancing blended teaching and achieving an optimal effect. However, cognitive engagement has increased the difficulty of measurement and intervention because of its implicit nature. Furthermore, problems such as solid subjectivity and single modes in past evaluation methods make it challenging to clarify students’ cognitive engagement. A blended classroom has the unique characteristics of complex elements and diverse learning contexts, and the method of measuring students’ cognitive engagement differs from that used in a single teaching environment. In addition, the internal connection based on knowledge experience between the individual online and offline learning processes cannot be ignored. Students have specific knowledge preparation when they enter the offline classroom.
Our aim is to improve students’ cognitive engagement in blended classrooms. First, it was necessary to establish a scientific measurement index system of students’ cognitive engagement. The blended classroom stage (i.e., online or offline) and process (i.e., the internal connection of knowledge in online self-learning and subsequent offline face-to-face learning) are considered to establish effective strategies for improving cognitive engagement. The practical goal is to develop appropriate teaching strategies and a digital teaching tool to improve students’ cognitive engagement in actual blended classrooms.
Specifically, in Study 1, a quantitative representation model of cognitive engagement in a blended classroom will be constructed by collecting multimodal data of learners in actual blended classrooms at different stages and integrating text and video analyses, eye tracking and psychometric indicators. Grey relational analysis and the entropy method will be used to calculate the weights of the evaluation index system. Study 2 will explore strategies for improving students’ cognitive engagement in blended classrooms from the perspectives of learning resources and instructional strategies. A series of empirical studies will be carried out to identify strategies for improving learners’ cognitive engagement in online, offline, and blended classroom processes. The roles of learning resources and generative learning strategies will be investigated in the online stage. Based on the process perspective of the internal connection between online self-learning and offline face-to-face learning stages, we will explore how to combine student problem-generating and teacher problem-scaffolding strategies effectively. We will also learn how to provide teacher feedback to learners with different cognitive levels. The effects of teacher feedback and student reflection on cognitive engagement will be investigated in an offline classroom setting. Thus, practical strategies for improving cognitive engagement can be developed according to the gold standard from empirical research. In Study 3, the effective teaching strategies proposed in the second study will be verified by tracking them in an actual blended classroom. Simultaneously, collaborative changes in instructional strategies and students’ cognitive structures can be obtained through a longitudinal cognitive network analysis, which will help form empirical evidence in the teaching environment. For Study 4, based on the research above, using multimodal data in the blended classroom and applying the neural network algorithm, we will establish a cognitive engagement classifier. By combining the trigger mechanism of instructional intervention strategies and classifier, the study will form a teaching assistant tool with the function of cognitive engagement identification and intervention strategy reminders in the blended classroom. We intend to utilize the cross-integration of information technology and empirical and applied research in educational psychology to realize an effective combination of scientific research and practical applications in blended teaching.

Key words: blended classroom, cognitive engagement, multi-modal measurement indices, intervention strategies

田媛, 聂昕晓, 刘海诺, 刘忠健, 房敏, 张琪. (2024). 混合课堂学生认知投入的多模态量化机制与干预. 心理科学进展 , 32(10), 1578-1592.

TIAN Yuan, NIE Xinxiao, LIU Hainuo, LIU Zhongjian, FANG Min, ZHANG Qi. (2024). Multi-modal quantitative assessment mechanism and intervention of learners’ cognitive engagement in blended classroom. Advances in Psychological Science, 32(10), 1578-1592.

参考文献

[1] 卜彩丽, 李飒, 王静, 张思, 董乐. (2022). 为深度学习而思:反思日志促进大学生元认知发展的实证研究.现代教育技术, 32(9), 73-81.
[2] 曹梅, 马悦. (2020). 翻转课堂课前深层次学习的问题生成策略研究.电化教育研究, 41(11), 101-107.
[3] 郝祥军, 王帆, 汪云华. (2019). 问题支架促进在线知识交互的途径假设与验证. 中国远程教育, 3, 34-42+92-93.
[4] 姜强, 潘星竹, 赵蔚, 刘红霞. (2018). 网络学习空间中教师激励风格对学习投入的影响研究——SDT中内部动机的中介效应.中国电化教育, 380, 7-16.
[5] 李克东, 赵建华. (2004). 混合学习的原理与应用模式.电化教育研究, 135, 1-6.
[6] 李卢一, 许蓉, 郑燕林. (2013). ARCS模型视角下网络学习反馈设计.现代远距离教育, 147, 66-71.
[7] 刘玲, 汪琼. (2021). 混合教学模式下学生学习投入的特点及影响因素研究.现代教育技术, 31(11), 80-86.
[8] 卢国庆, 刘清堂, 郑清, 谢魁. (2021). 智能教室中环境感知及自我效能感对个体认知投入的影响研究.远程教育杂志, 39(3), 84-93.
[9] 马志强, 岳芸竹. (2020). 面向即时数据采集与分析的学习投入纵向研究——基于经验取样法与交叉滞后分析的综合应用.电化教育研究, 41(4), 71-77.
[10] 皮忠玲, 章仪, 杨九民. (2019). 教师手势对视频学习的影响及其认知神经机制. 中国电化教育, 387, 103-110+129.
[11] 钱研, 陈晓慧. (2015). 南加州大学翻转课堂设计原则及其启示.中国电化教育, 341, 99-103.
[12] 沈霞娟, 张宝辉, 冯锐. (2022). 混合学习环境下的深度学习活动研究: 设计、实施与评价的三重奏.电化教育研究, 43(1), 106-112.
[13] 师亚飞, 童名文, 王建虎, 孙佳, 戴红斌, 魏艳涛. (2021). 混合同步学习: 演进, 价值与未来议题.电化教育研究, 42(12), 100-107.
[14] 田浩, 武法提. (2022). 混合场景下协作认知投入的多模态表征与分析路径研究.远程教育杂志, 40(4), 35-44.
[15] 田阳, 陈鹏, 黄荣怀, 曾海军. (2019). 面向混合学习的多模态交互分析机制及优化策略.电化教育研究, 40(9), 67-74.
[16] 田媛, 亓栀, 黄湘琳, 向虹钰, 汪颖. (2021). 社会线索促进在线学习的认知神经机制.电化教育研究, 42(2), 63-69.
[17] 田媛, 席玉婷. (2020). 高校混合课堂教学模式的应用研究.中国大学教学, 8, 78-86.
[18] 王雪, 高泽红, 徐文文, 张蕾. (2021a). 反馈的情绪设计对视频学习的影响机制研究.电化教育研究, 42(3), 69-74.
[19] 王雪, 张蕾, 杨文亚, 卢鑫, 徐文文, 高泽红. (2021b). 在线学习资源如何影响学业情绪和学习效果——基于控制—价值理论的元分析. 现代远程教育研究, 33(5), 82-93+ 102.
[20] 王志军, 杨阳. (2019). 认知网络分析法及其应用案例分析.电化教育研究, 40(6), 27-34.
[21] 温慧群, 穆肃. (2023). 殊途如何同归?——不同复杂度混合教学实践效果的分析.中国远程教育, 43(2), 64-72.
[22] 伍绍杨, 彭正梅. (2021). 迈向更有效的反馈: 哈蒂“可见的学习”的模式.开放教育研究, 27(4), 27-40.
[23] 吴忭, 王戈, 盛海曦. (2018). 认知网络分析法: STEM教育中的学习评价新思路.远程教育杂志, 36(6), 3-10.
[24] 杨九民, 张锐, 蒋玲, 黄磊. (2011). 基于博客提升师范生反思能力的策略及其研究.中国电化教育, 298, 62-66.
[25] 杨九民, 章仪, 徐珂, 皮忠玲. (2021). 学习策略对视频学习的影响: 想象、绘图和自我解释策略.电化教育研究, 42(10), 40-47.
[26] 张利钊, 杜旭, 李浩, 谢艺乾, 唐野野. (2022). 基于多模态数据的学习投入评估方法分析.电化教育研究, 43(10), 72-78.
[27] 张思, 何晶铭, 上超望, 夏丹, 胡泉. (2020). 面向在线学习协同知识建构的认知投入分析模型及应用.远程教育杂志, 38(4), 95-104.
[28] 赵国庆, 杨宣洋, 熊雅雯. (2019). 论思维可视化工具教学应用的原则和着力点. 电化教育研究, 40(9), 59-66+82.
[29] 周媛, 韩彦凤. (2018). 混合学习活动中学习者学习投入的研究.电化教育研究, 39(11), 99-105.
[30] Anthonysamy L., Koo A. C., & Hew S. H. (2020). Self- regulated learning strategies and non-academic outcomes in higher education blended learning environments: A one decade review. Education and Information Technologies, 25, 3677-3704. https://doi.org/10.1007/s10639-020-10134-2
[31] Appleton J. J., Christenson S. L., Kim D.,& Reschly, A. L.(2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 442006.04.002
[32] Baceviciute S., Lucas G., Terkildsen T., & Makransky G. (2022). Investigating the redundancy principle in immersive virtual reality environments: An eye-tracking and EEG study. Journal of Computer Assisted Learning, 38(1), 120-136. https://doi.org/10.1111/jcal.12595
[33] Bredow C. A., Roehling P. V., Knorp A. J., & Sweet A. M. (2021). To flip or not to flip? A meta-analysis of the efficacy of flipped learning in higher education. Review of Educational Research, 91(6), 878-918. https://doi.org/10.3102/00346543211019122
[34] Chi M. T.H., & Wylie, R.(2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 492014.965823
[35] Dubovi, I. (2022). Cognitive and emotional engagement while learning with VR: The perspective of multimodal methodology. Computers & Education, 183, Article 104495. https://doi.org/10.1016/j.compedu.2022.104495
[36] Fiorella L.,& Mayer, R. E. (2015). Learning as a generative activity: Eight learning strategies that promote understanding Cambridge University Press https://doiorg/101017/CBO9781107707085
[37] Fyfe E. R., Rittle-Johnson B., & DeCaro M. S. (2012). The effects of feedback during exploratory mathematics problem solving: Prior knowledge matters. Journal of Educational Psychology, 104(4), 1094-1108. https://doi.org/10.1037/a0028389
[38] Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95-105. https://doi.org/10.1016/j.iheduc.2004.02.001
[39] Gašević D., Adesope O., Joksimović S.,& Kovanović, V.(2015). Externally-facilitated regulation scaffolding and role assignment to develop cognitive presence in asynchronous online discussions. The Internet and Higher Education, 24, 53-65. https://doi.org/10.1016/j.iheduc.2014.09.006
[40] Ge, X., & Er, N. (2005). An online support system to scaffold real-world problem solving. Interactive Learning Environments, 13(3), 139-157. https://doi.org/10.1080/10494820500382893
[41] Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14-30. https://doi.org/10.1080/00461520.2014.989230
[42] Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487
[43] Jusoff, K., & Khodabandelou, R. (2009). Preliminary study on the role of social presence in blended learning environment in higher education. International Education Studies, 2(4), 79-83. https://doi.org/10.5539/ies.v2n4p79
[44] Krause, K. L., & Coates, H. (2008). Students’ engagement in first-year university. Assessment & Evaluation in Higher Education, 33(5), 493-505. https://doi.org/10.1080/02602930701698892
[45] Lee, C. (2020). A study of adolescent English learners’ cognitive engagement in writing while using an automated content feedback system. Computer Assisted Language Learning, 33(1-2), 26-57. https://doi.org/10.1080/09588221.2018.1544152
[46] Lee V. R., Fischback L.,& Cain, R.(2019). A wearables- based approach to detect and identify momentary engagement in afterschool Makerspace programs. Contemporary Educational Psychology, 59, Article 101789. https://doi.org/10.1016/j.cedpsych.2019.101789
[47] Li S., Lajoie S. P., Zheng J., Wu H.,& Cheng, H.(2021). Automated detection of cognitive engagement to inform the art of staying engaged in problem-solving. Computers & Education, 163, Article 104114. https://doi.org/10. 1016/j.compedu.2020.104114
[48] Liao C. H.,& Wu, J. Y.(2022). Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Computers & Education, 190, Article 104599. https://doi.org/10.1016/j.compedu.2022.104599
[49] Lightner C. A.,& Lightner-Laws, C. A.(2016). A blended model: Simultaneously teaching a quantitative course traditionally, online, and remotely. Interactive Learning Environments, 242013.841262
[50] Liu S., Liu S., Liu Z., Peng X.,& Yang, Z.(2022). Automated detection of emotional and cognitive engagement in MOOC discussions to predict learning achievement. Computers & Education, 181, Article 104461. https://doi.org/10.1016/j.compedu.2022.104461
[51] Miller B. W.(2015). Using reading times and eye-movements to measure cognitive engagement. Educational Psychologist, 50(1), 31-42. https://doi.org/10.1080/00461520.2015.1004068
[52] Müller C.,& Mildenberger, T.(2021). Facilitating flexible learning by replacing classroom time with an online learning environment: A systematic review of blended learning in higher education. Educational Research Review, 34, Article 100394. https://doi.org/10.1016/j.edurev.2021.100394
[53] Porter W. W., Graham C. R., Spring K. A.,& Welch, K. R.(2014). Blended learning in higher education: Institutional adoption and implementation. Computers & Education, 75, 185-195. https://doi.org/10.1016/j.compedu.2014.02.011
[54] Rotgans, J. I., & Schmidt, H. G. (2011). Cognitive engagement in the problem-based learning classroom. Advances in Health Sciences Education, 16(4), 465-479. https://doi.org/10.1007/s10459-011-9272-9
[55] Sharma, K., & Giannakos, M. (2020). Multimodal data capabilities for learning: What can multimodal data tell us about learning? British Journal of Educational Technology, 51(5), 1450-1484. https://doi.org/10.1111/bjet.12993
[56] Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153-189. https://doi.org/10.3102/0034654307313795
[57] Sinatra G. M., Heddy B. C.,& Lombardi, D.(2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 502014.1002924
[58] Walker C. O., Greene B. A.,& Mansell, R. A.(2006). Identification with academics, intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement. Learning and Individual Differences, 162005.06.004
[59] Wang S. L.,& Wu, P. Y.(2008). The role of feedback and self-efficacy on web-based learning: The social cognitive perspective. Computers & Education, 512008.03.004
[60] Xie K., Vongkulluksn V. W., Lu L.,& Cheng, S. L.(2020). A person-centered approach to examining high-school students’ motivation, engagement and academic performance. Contemporary Educational Psychology, 62, Article 101877. https://doi.org/10.1016/j.cedpsych.2020.101877
[61] Xu B., Chen N. S.,& Chen, G.(2020). Effects of teacher role on student engagement in WeChat-Based online discussion learning. Computers & Education, 157, Article 103956. https://doi.org/10.1016/j.compedu.2020.103956
[62] Xu X., Shi Z., Bos N. A.,& Wu, H.(2023). Student engagement and learning outcomes: An empirical study applying a four-dimensional framework. Medical Education Online, 282023.2268347
[63] Yu F. Y.,& Kuo, C. W.(2024). A systematic review of published student question-generation systems: Supporting functionalities and design features. Journal of Research on Technology in Education, 562022.2119448