Multi-modal quantitative assessment mechanism and intervention of learners’ cognitive engagement in blended classroom

doi:10.3724/SP.J.1042.2024.01578

Abstract

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

CLC Number:

B849: G44

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

Figures/Tables 9

References 63

[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, 44(5), 427-445. https://doi.org/10.1016/j.jsp.2006.04.002
[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, 49(4), 219-243. https://doi.org/10.1080/00461520.2014.965823
[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://doi.org/10.1017/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, 24(1), 224-238. https://doi.org/10.1080/10494820.2013.841262
[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, 50(1), 1-13. https://doi.org/10.1080/00461520.2014.1002924
[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, 16(1), 1-12. https://doi.org/10.1016/j.lindif.2005.06.004
[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, 51(4), 1589-1598. https://doi.org/10.1016/j.compedu.2008.03.004
[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, 28(1), Article 2268347. https://doi.org/10.1080/10872981.2023.2268347
[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, 56(2), 172-195. https://doi.org/10.1080/15391523.2022.2119448

维度	指标	编码	具体观测度量
行为参与	平均学习时长(线上、线下)	DT	$ D T=\frac{T_{1}+T_{2}+\ldots+T_{i}}{i}$
	登录次数(抬头次数)	GT	学习平台统计频次(课程录像频次)
	参与答题率(主动、被动回答)	AR	$ A=\omega_{1} A_{I}+\omega_{2} A_{P}$
认知建构	发帖质量	PQ	1~5计分
	回帖质量(答题质量)	RQ	1~5计分
	自我解释回答	SR	1~5计分
	眼动指标	FD	注视时长
		SC	扫视次数
		PD	瞳孔直径差$ \Delta D=D_{1}-D_{2}$
社会互动	点出度	OD	$ \text { outDegree }_{\mathrm{i}}=\sum_{\mathrm{j}=1}^{\mathrm{n}} \mathrm{Z}_{\mathrm{ij}}$
	点入度	ID	$ \text { inDegree }_{i}=\sum_{j=1}^{n} Z_{\mathrm{ji}}$
	点度中心性	DC	$ C_{i}=\sum_{j=1}^{n}\left(z_{i j}+Z_{j i}\right) / \sum_{i=1}^{n} \sum_{j=1}^{n} z_{i j}$

维度	指标	编码	具体观测度量
行为参与	平均学习时长(线上、线下)	DT	$ D T=\frac{T_{1}+T_{2}+\ldots+T_{i}}{i}$
	登录次数(抬头次数)	GT	学习平台统计频次(课程录像频次)
	参与答题率(主动、被动回答)	AR	$ A=\omega_{1} A_{I}+\omega_{2} A_{P}$
认知建构	发帖质量	PQ	1~5计分
	回帖质量(答题质量)	RQ	1~5计分
	自我解释回答	SR	1~5计分
	眼动指标	FD	注视时长
		SC	扫视次数
		PD	瞳孔直径差$ \Delta D=D_{1}-D_{2}$
社会互动	点出度	OD	$ \text { outDegree }_{\mathrm{i}}=\sum_{\mathrm{j}=1}^{\mathrm{n}} \mathrm{Z}_{\mathrm{ij}}$
	点入度	ID	$ \text { inDegree }_{i}=\sum_{j=1}^{n} Z_{\mathrm{ji}}$
	点度中心性	DC	$ C_{i}=\sum_{j=1}^{n}\left(z_{i j}+Z_{j i}\right) / \sum_{i=1}^{n} \sum_{j=1}^{n} z_{i j}$

认知活动类型	学习行为	具体表现(标签)	取得的认知和学习效果
被动	被动接受知识	无目的朗读复述, 被动听其他人讲话, 看学习材料	回忆, 获得浅层的理解
主动	主动进行认知操作	对某个知识点进行要有目的地注意和重复; 对视频暂停或快进; 对教师讲解重点记笔记	整合新旧知识, 中层理解
建构	产出学习材料以外的新知识	生成性学习, 自我解释、绘图、测试	深层理解、迁移, 在新情境中应用
互动	与同伴进行对话	团队争辩、相互问答	创新, 有创造新知识的潜力

认知活动类型	学习行为	具体表现(标签)	取得的认知和学习效果
被动	被动接受知识	无目的朗读复述, 被动听其他人讲话, 看学习材料	回忆, 获得浅层的理解
主动	主动进行认知操作	对某个知识点进行要有目的地注意和重复; 对视频暂停或快进; 对教师讲解重点记笔记	整合新旧知识, 中层理解
建构	产出学习材料以外的新知识	生成性学习, 自我解释、绘图、测试	深层理解、迁移, 在新情境中应用
互动	与同伴进行对话	团队争辩、相互问答	创新, 有创造新知识的潜力