教师在线教学准备与学生学习效果的关系探究

蔡红红

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

蔡红红

华东师范大学高等教育研究所，上海 200062

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”

计量
- 文章访问数: 338
- HTML全文浏览量: 376
- PDF下载量: 72
- 被引次数: 0
出版历程
- 刊出日期: 2021-07-01

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

Cai Honghong

Institute of Higher Education, East China Normal University, Shanghai 200062, China

摘要

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。
- 线上学习效果 /
- 教师在线教学准备 /
- 学习者控制 /
- 学业情绪 /
- 研究生
Abstract: In order to explore the influence and mechanism of teachers’ e-readiness on the graduate students’ online learning effect, the research basing on the control-value theory employed a structural equation model to analyze the survey data. The results showed that teachers’ e-readiness not only had positive effect on graduate students’ online learning effect, but also through the independent mediating effect of learner control, academic emotions and the chain mediating effect of “learner control and academic emotions” indirectly predicted graduate students’ online learning effect respectively. At the same time, the total indirect effect was slightly larger than direct effect. Among the three specific indirect effects, the independent indirect effect of learner control was the largest. The results of this research suggest that, online teaching training should be provided for teachers, teachers’ e-readiness and the quality of online courses should be improved; it is critical to assess graduate students’ learner control on online learning, as well as provide adequate guidance for them. At last, attention should be paid to the graduate students’ academic emotions online learning, negative academic emotions should be intervened in time.
- online learning effect /
- teachers' e-readiness /
- learner control /
- academic emotions /
- graduate students

HTML全文

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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	女	9552	61.86	学科	社会	5249	33.99
性别	男	5889	38.14		工程	5181	33.55
生源地	直辖市/省会城市	3048	19.74		自然	2337	15.14
	其他地级市	3568	23.11		人文	2674	17.32
	县级市/县城	3603	23.33	学校	“一流大学”建设高校	5665	36.69
	乡镇和农村	5222	33.82		“一流学科”建设高校	4136	26.79
年级	硕士生	14286	92.52		非“双一流”建设高校	5640	36.53
年级	博士生	1155	7.48

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
变量	平均值（M）	标准差（SD）	1	2	3	4
1. 教师在线教学准备	4.03	0.598	—
2. 学习者控制	3.52	0.709	0.549***	—
3. 学业情绪	2.78	0.911	−0.331***	−0.376***	—
4. 学习效果	3.41	0.736	0.436***	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
变量	因子载荷范围	CR	AVE	1	2	3	4
1. 教师在线教学准备	0.781-0.885	0.864	0.680	0.680
2. 学习者控制	0.770-0.887	0.891	0.673	0.301	0.673
3. 学业情绪	0.733-0.898	0.891	0.672	0.110	0.141	0.672
4. 学习效果	0.829-0.918	0.943	0.769	0.190	0.227	0.126	0.769
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	Z	Bias-Corrected95% CI		相对效应百分比
效应类别	效应值	Boot标准误	Z	下限	上限	相对效应百分比
直接效应	0.313***	0.017	18.412	0.280	0.347	46.58%
总间接效应	0.359***	0.013	27.615	0.335	0.386	53.42%
总效应	0.672***	0.014	48.000	0.645	0.699	100.00%
特定间接效应
TR→LC→LE	0.265***	0.012	22.250	0.242	0.292	39.43%
TR→AE→LE	0.041***	0.004	10.500	0.035	0.049	6.10%
TR→LC→AE→LE	0.053***	0.004	13.250	0.046	0.060	7.89%
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

参考文献(56)

[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.