中国人文社会科学核心期刊

中文社会科学引文索引(CSSCI)来源期刊

中文核心期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

教师在线教学准备与学生学习效果的关系探究

蔡红红

蔡红红. 教师在线教学准备与学生学习效果的关系探究[J]. 华东师范大学学报(教育科学版), 2021, 39(7): 27-37. doi: 10.16382/j.cnki.1000-5560.2021.07.003
引用本文: 蔡红红. 教师在线教学准备与学生学习效果的关系探究[J]. 华东师范大学学报(教育科学版), 2021, 39(7): 27-37. doi: 10.16382/j.cnki.1000-5560.2021.07.003
Cai Honghong. Research on the Correlation of Teachers’ e-Readiness and Students’ Learning Effect: the Mediation Effect of Learner Control and Academic Emotions[J]. Journal of East China Normal University (Educational Sciences), 2021, 39(7): 27-37. doi: 10.16382/j.cnki.1000-5560.2021.07.003
Citation: Cai Honghong. Research on the Correlation of Teachers’ e-Readiness and Students’ Learning Effect: the Mediation Effect of Learner Control and Academic Emotions[J]. Journal of East China Normal University (Educational Sciences), 2021, 39(7): 27-37. doi: 10.16382/j.cnki.1000-5560.2021.07.003

教师在线教学准备与学生学习效果的关系探究

doi: 10.16382/j.cnki.1000-5560.2021.07.003
基金项目: 国家自然科学基金面上项目“高水平大学教师的职业压力、学术激情与活力研究”(71774055);文化名家暨“四个一批”人才工程资助项目“基于学生与学习变化的大学教师教与学的变革”

Research on the Correlation of Teachers’ e-Readiness and Students’ Learning Effect: the Mediation Effect of Learner Control and Academic Emotions

  • 摘要: 为探究教师在线教学准备对研究生线上学习效果的影响及作用机制,本研究基于控制—价值理论构建结构方程模型,对调查数据展开分析。研究发现,教师在线教学准备能够直接显著预测研究生线上学习效果,也能分别通过研究生的学习者控制、学业倦怠情绪的独立中介作用和学习者控制与学业倦怠情绪的链式中介作用间接预测学习效果,且总间接效应略大于直接效应。在三个特定间接效应中,学习者控制的独立间接效应最大。为提高研究生线上学习效果,应加强教师线上教学培训,提高教师在线教学准备度,改善线上课程教学质量;评估研究生线上学习的自我控制度,并提供充分指导;关注研究生线上学习的学业情绪,对消极学业情绪进行及时干预。
  • 图  1  教师在线教学准备与研究生学习效果关系的模型示意图

    图  2  教师在线教学准备对学生学习效果影响的结构方程模型图

    表  1  样本特征(N=15441)

    变量名选项N百分比(%)变量名选项N百分比(%)
    性别955261.86学科社会524933.99
    588938.14工程518133.55
    生源地直辖市/省会城市304819.74自然233715.14
    其他地级市356823.11人文267417.32
    县级市/县城360323.33学校“一流大学”建设高校566536.69
    乡镇和农村522233.82“一流学科”建设高校413626.79
    年级硕士生1428692.52非“双一流”建设高校564036.53
    博士生11557.48
    下载: 导出CSV

    表  2  变量的描述性统计与相关性

    变量平均值(M)标准差(SD)相关性
    1234
    1. 教师在线教学准备4.030.598
    2. 学习者控制3.520.7090.549***
    3. 学业情绪2.780.911−0.331***−0.376***
    4. 学习效果3.410.7360.436***0.476***−0.355***
      注:*** P< 0.001。
    下载: 导出CSV

    表  3  各变量的信度与效度

    变量因子载荷范围收敛效度区别效度
    CRAVE1234
    1. 教师在线教学准备0.781-0.8850.8640.6800.680
    2. 学习者控制0.770-0.8870.8910.6730.3010.673
    3. 学业情绪0.733-0.8980.8910.6720.1100.1410.672
    4. 学习效果0.829-0.9180.9430.7690.1900.2270.1260.769
    建议值>0.6>0.6>0.5对角线数值应大于对应的行和列的相关系数平方
      注:区别效度区间对角线粗体字为AVE数值,下三角为皮尔森相关系数的平方。
    下载: 导出CSV

    表  4  教师在线教学准备对研究生线上学习效果的直接效应、间接效应与总效应

    效应类别效应值Boot标准误ZBias-Corrected95% CI相对效应百分比
    下限上限
    直接效应0.313***0.01718.4120.2800.34746.58%
    总间接效应0.359***0.01327.6150.3350.38653.42%
    总效应0.672***0.01448.0000.6450.699100.00%
    特定间接效应
    TR→LC→LE0.265***0.01222.2500.2420.29239.43%
    TR→AE→LE0.041***0.00410.5000.0350.0496.10%
    TR→LC→AE→LE0.053***0.00413.2500.0460.0607.89%
      注:报告的是非标准化效应值;***p<0.001;TR表示教师在线教学准备,LC表示学习者控制,AE表示学业情绪,LE表示学习效果。
    下载: 导出CSV
  • [1] 畅军亮, 吴丹. (2016). 基于扎根理论的大学生学习倦怠研究—以X大学为例. 高教探索,(8),62—65+79. doi:  10.3969/j.issn.1673-9760.2016.08.010
    [2] 李文昊, 祝智庭. (2020). 改善情感体验: 缓解大规模疫情时期在线学习情绪问题的良方. 中国电化教育,(5),22—26+79. doi:  10.3969/j.issn.1006-9860.2020.05.005
    [3] 罗乐, 鲁朋举, 余林. (2009). 学业情绪及其相关研究. 教育与教学研究,(6),27—29+100. doi:  10.3969/j.issn.1674-6120.2009.06.009
    [4] 温忠麟, 侯杰泰, 马什赫伯特. (2004). 结构方程模型检验:拟合指数与卡方准则. 心理学报,(2),186—194.
    [5] 邬大光, 李文 . (2020). 我国高校大规模线上教学的阶段性特征—基于对学生、教师、教务人员问卷调查的实证研究. 华东师范大学学报(教育科学版),(7),1—30.
    [6] 阎光才. (2020). 我国本科教与学过程的特征与问题分析. 中国高教研究,(5),1—8.
    [7] 袁振国. (2020). “疫情下的信息技术与在线教学”笔谈. 基础教育,(3),48—60. doi:  10.3969/j.issn.1005-2232.2020.03.006
    [8] Artino Jr, A. R., & Jones ll, K. D. (2012). Exploring the complex relations between achievement emotions and self-regulated learning behaviors in online learning. The Internet and Higher Education, 15(3), 170—175. doi:  10.1016/j.iheduc.2012.01.006
    [9] Baker, R. S., D’Mello, S. K., Rodrigo, M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human Computer Studies, 68(4), 223—241. doi:  10.1016/j.ijhcs.2009.12.003
    [10] Bates, A. (1995). Technology, open learning and distance education. New York: Routledge, 370.
    [11] Bovy, R. C. (1981). Successful instructional methods: a cognitive information processing approach. ECTJ., 29(4), 203—217.
    [12] Conrad, D. L. (2002). Engagement, excitement, anxiety, and fear: Learners’ experiences of starting an online course. The American journal of distance education, 16(4), 205—226. doi:  10.1207/S15389286AJDE1604_2
    [13] Cotton, S., Dollard, M., & De Jonge, J. (2002). Stress and student job design: Satisfaction, well-being, and performance in university students. International Journal of Stress Management, 9(3), 147—162. doi:  10.1023/A:1015515714410
    [14] Cushman, S., & West, R. (2006). Precursors to college student burnout: Developing a typology of understanding. Qualitative research reports in communication, 7(1), 23—31. doi:  10.1080/17459430600964638
    [15] Dillenbourg, P. (2008). Integrating technologies into educational ecosystems. Distance Education, 29(2), 127—140. doi:  10.1080/01587910802154939
    [16] D'Mello, S. K., Craig, S. D., Sullins, J., & Graesser, A. C. (2006). Predicting affective states expressed through an emote-aloud procedure from AutoTutor’s mixed-initiative dialogue. International Journal of Artificial Intelligence in Education, 16(1), 3—28.
    [17] Downing, J. J., & Dyment, J. E. (2013). Teacher educators’ readiness, preparation, and perceptions of preparing preservice teachers in a fully online environment: An exploratory study. The teacher educator, 48(2), 96—109. doi:  10.1080/08878730.2012.760023
    [18] Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration?. Educational Technology Research and Development, 53(4), 25—39. doi:  10.1007/BF02504683
    [19] Eslaminejad, T., Masood, M., & Ngah, N. A. (2010). Assessment of instructors’ readiness for implementing e-learning in continuing medical education in Iran. Medical teacher, 32(10), e407—e412. doi:  10.3109/0142159X.2010.496006
    [20] Gaines, J., & Jermier, J. M. (1983). Emotional exhaustion in a high stress organization. Academy of Management journal, 26(4), 567—586.
    [21] Gay, G. (1986). Interaction of learner control and prior understanding in computer-assisted video instruction. Journal of educational psychology, 78(3), 225. doi:  10.1037/0022-0663.78.3.225
    [22] Gay, G. H. (2016). An assessment of online instructor e-learning readiness before, during, and after course delivery. Journal of Computing in Higher Education, 28(2), 199—220. doi:  10.1007/s12528-016-9115-z
    [23] Glassett, K., & Schrum, L. (2009). Teacher beliefs and student achievement in technology-rich classroom environments. International Journal of Technology in Teaching and Learning, 5(2), 138—153.
    [24] Goetzfried, L., & Hannafin, M. J. (1985). The effect of the locus of CAI control strategies on the learning of mathematics rules. American Educational Research Journal, 22(2), 273—278. doi:  10.3102/00028312022002273
    [25] Goldsworthy, R. (2000). Designing instruction for emotional intelligence. Educational Technology, 40(5), 43—58.
    [26] Greenfield, D. G., & Codding, P. A. (1985). Competency-based vs linear computer instruction of music fundamentals. Journal of Computer-Based Instruction, 12(4), 108—110.
    [27] Hannafin, M. J. (1984). Guidelines for using locus of instructional control in the design of computer-assisted instruction. Journal of instructional development, 7(3), 6—10. doi:  10.1007/BF02905753
    [28] Hacker, D. J., & Niederhauser, D. S. (2000). Promoting deep and durable learning in the online classroom. New Directions for Teaching and Learning, 84, 53—63.
    [29] Hair, J. F., Jr., Anderson, R. E., Tatham, R. L. et al. (2009). Multivariate data analysis: Multivariate data analysis (7th Edition). Englewood Cliffs, NJ: Prentice Hall, 618-620.
    [30] Hu, L. T., and Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1—55. doi:  10.1080/10705519909540118
    [31] Hung, M. L., Chou, C., Chen, C. H., & Own, Z. Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3), 1080—1090.
    [32] Keller, J. M., & Reigeluth, C. M. (1983). Instructional design theories and models: An overview of their current status. Motivational Design of Instruction, by Reigeluth, CM, 384-434.
    [33] Kulik, J. A., Kulik, C. L. C., & Cohen, P. A. (1980). Effectiveness of computer-based college teaching: A meta-analysis of findings. Review of educational research, 50(4), 525—544. doi:  10.3102/00346543050004525
    [34] Lepper, M. R. (1985). Microcomputers in education: Motivational and social issues. American psychologist, 40(1), 1. doi:  10.1037/0003-066X.40.1.1
    [35] Linnenbrink, E. A. (2007). The role of affect in student learning: A multi-dimensional approach to considering the interaction of affect, motivation, and engagement. Emotion in education. Academic Press, 107-124.
    [36] MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention science, 1(4), 173—181. doi:  10.1023/A:1026595011371
    [37] Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of organizational behavior, 2(2), 99—113. doi:  10.1002/job.4030020205
    [38] Marsh, H.W. (2001). Distinguishing between good (useful) and bad workloads on students’ evaluations of teaching. American Educational Research Journal, 38(1), 183—212. doi:  10.3102/00028312038001183
    [39] Martin, F., Budhrani, K., & Wang, C. (2019). Examining Faculty Perception of Their Readiness to Teach Online. Online Learning, 23(3), 97—119.
    [40] McKnight, K., O’Malley, K., Ruzic, R., Horsley, M. K., Franey, J. J., & Bassett, K. (2016). Teaching in a digital age: How educators use technology to improve student learning. Journal of research on technology in education, 48(3), 194—211. doi:  10.1080/15391523.2016.1175856
    [41] Naveed, Q. N., Qureshi, M. R. N., Tairan, N., Mohammad, A., Shaikh, A., Alsayed, A. O.,.. & Alotaibi, F. M. (2020). Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making. Plos one, 15(5), 1—25.
    [42] Ncube, S., Dube, L., & Ngulube, P. (2014). E-learning readiness among academic staff in the Department of Information Science at the University of South Africa. Mediterranean Journal of Social Sciences, 5(16), 357—366.
    [43] Northrup, P. T. (2002). Online learners’ preferences for interaction. The Quarterly Review of Distance Education, 3(2), 219—226.
    [44] Papert, S. (1980). Mindstorms: children, computers, and powerful ideas. New York: Basic Books, 99.
    [45] Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational psychologist, 37(2), 91—105. doi:  10.1207/S15326985EP3702_4
    [46] Penna, M., & Stara, V. (2007). The failure of e-learning: why should we use a learner centred design. Journal of E-learning and Knowledge Society, 3(2), 127—135.
    [47] Phan, T. T. N., & Dang, L. T. T. (2017). Teacher Readiness for Online Teaching: A Critical Review. International Journal Open Distance E-Learn, 3(1), 1—16.
    [48] Rainford, J. (2021). Moving widening participation outreach online: challenge or opportunity?. Perspectives: Policy and Practice in Higher Education, 25(1), 2—6. doi:  10.1080/13603108.2020.1785968
    [49] Reeves, T. C., & Reeves, P. M. (1997). Effective dimensions of interactive learning on the World Wide Web. Web-based instruction, 59—66.
    [50] Regmi, K., & Jones, L. (2020). A systematic review of the factors–enablers and barriers–affecting e-learning in health sciences education. BMC medical education, 20, 1—18. doi:  10.1186/s12909-019-1842-1
    [51] Rohayani, A. H. (2015). A literature review: Readiness factors to measuring e-learning readiness in higher education. Procedia Computer Science, 59, 230—234. doi:  10.1016/j.procs.2015.07.564
    [52] Shyu, H. Y., & Brown, S. W. (1992). Learner control versus program control in interactive videodisc instruction: What are the effects in procedural learning. International Journal of Instructional Media, 19(2), 85—95.
    [53] Spitzer, D.R. (2001). Don’t forget the high touch with the high tech in distance learning. Educational Technology, 41(2), 51—55.
    [54] Stein, N. L., Hernandez, M., & Trabasso, T. (2008). Advances in modeling emotions and thought: The importance of developmental, online, and multilevel analysis. In M. Lewis, J. M. Haviland-Jones, & L. F. Barrett (Eds.), Handbook of emotions. New York, NY: Guilford Press, 574−586.
    [55] Tennyson, R. D. (1981). Use of adaptive information for advisement in learning concepts and rules using computer-assisted instruction. American Educational Research Journal, 18(4), 425—438. doi:  10.3102/00028312018004425
    [56] Tricker, T., M. Rangecroft, P. Long, & P. Gilroy. (2001). Evaluating distance education courses: The student perception. Assessment & Evaluation in Higher Education, 26(2), 165—177.
  • 加载中
图(2) / 表(4)
计量
  • 文章访问数:  364
  • HTML全文浏览量:  416
  • PDF下载量:  76
  • 被引次数: 0
出版历程
  • 刊出日期:  2021-07-01

目录

    /

    返回文章
    返回