数据驱动学习在学术英语词汇教学中的应用

摘要/Abstract

摘要：

本研究是以数据驱动学习(DDL)为理论基础,进行学术英语词汇教学的一次实践探索。本研究选择上海市某高校学术英语听说课堂上的非英语专业本科生为研究对象,进行为期八周的学术英语词汇DDL教学实验,并采用问卷调查和课堂观察相结合的三角验证法研究学生对该学习模式的认可度以及在实施过程中存在的局限性。研究发现DDL确实能够激发学生学术英语词汇的学习兴趣,学生对该学习模式的认可度较高;但是学生普遍认为根据语料语境猜测词义有一定的难度。因此,语料难度的把握应当引起教师的重视。在今后的学术英语词汇教学过程中,教师可以尝试使用DDL模式来提高学术英语词汇教学的创新性和科学性。

关键词:学术英语,数据驱动学习,词汇教学

Abstract:

This study is a practical exploration of academic English vocabulary teaching based on data-driven learning (DDL). DDL (Tribble & Jones 1990; Stevens 1991), based on a corpus index, is a form of computer-assisted instruction that encourages students to actively understand the meaning and usage patterns of vocabulary by observing real corpus. The authors of this study adopted a convenient sampling method to select non-English major undergraduates in the academic English listening and speaking class of a university in Shanghai as research participants, and conducted an eight-week academic English vocabulary teaching experiment by using DDL. The triangulation method combining questionnaire survey and classroom observation was used to study the acceptance of DDL and the limitations in the implementation process. Findings showed that DDL could really stimulate students’ interest in academic English vocabulary learning, and it was highly recognized by students. But students also believe that sometimes it is difficult to guess word meaning from corpus context. Therefore, the difficulty of corpus should attract the attention of teachers. In the future academic English vocabulary teaching, teachers can use DDL mode to improve the innovation and scientificalness of academic English vocabulary teaching.

Key words:academic English,data-driven learning,vocabulary teaching

中图分类号:

H319

刘芹, 可庆宝. 数据驱动学习在学术英语词汇教学中的应用[J]. 当代外语研究, 2020, 20(1): 58-67.

LIU Qin, KE Qingbao. The Effect of Data-driven Learning on Academic English Vocabulary Teaching[J]. Contemporary Foreign Languages Studies, 2020, 20(1): 58-67.

图/表3

图1

表1

问卷调查频数统计

	总数	非常赞同	比较赞同	一般	不赞同	非常反对	平均数	众数	标准差
Q1	44	24(54.5%)	14(31.8%)	6(13.6%)	0	0	4.41	5	0.73
Q2	44	21(47.7%)	16(36.4%)	6(13.6%)	1(2.3%)	0	4.30	5	0.80
Q3	44	22(50%)	14(31.8%)	8(18.2%)	0	0	4.32	5	0.77
Q4	44	25(56.8%)	13(29.5%)	6(13.6%)	0	0	4.43	5	0.73
Q5	44	21(47.7%)	17(38.6%)	6(13.6%)	0	0	4.34	5	0.71
Q6	44	26(59.1%)	12(27.3%)	6(13.6%)	0	0	4.45	5	0.73
Q7	44	13(29.5%)	21(47.7%)	9(20.5%)	1(2.3%)	0	4.05	4	0.78
Q8	44	3(6.8%)	10(22.7%)	22(50.0%)	6(13.6%)	3(6.8%)	3.09	3	0.96
Q9	44	9(20.5%)	15(34.1%)	17(38.6%)	3(6.8%)	0	3.68	3	0.88
Q10	44	6(13.6%)	15(34.1%)	15(34.1%)	7(15.9%)	1(2.3%)	3.41	4	1.00
Q11	44	14(31.8%)	19(43.2%)	8(18.2%)	2(4.5%)	1(2.3%)	3.98	4	0.95
Q12	44	15(34.1%)	11(25.0%)	14(31.8%)	1(2.3%)	0	3.83	5	1.06
Q13	44	15(34.1%)	18(40.9%)	11(25.0%)	0	0	4.09	4	0.77
Q14	44	12(27.3%)	18(40.9%)	12(27.3%)	2(4.5%)	0	3.91	4	0.86

表1

表2

难度认可度与兴趣相关性检验

			Q8	Q9	Q10	Q13	Q14
斯皮尔曼 Rho	Q8	相关系数	1.000	0.473^*	0.657^*	0.030	0.045
		显著性(双尾)		0.001	000	0.849	0.772
		个案数	44	44	44	44	44
	Q9	相关系数	0.473^*	1.000	0.564^**	-0.098	-0.034
		显著性(双尾)	0.001	.	000	0.527	0.825
		个案数	44	44	44	44	44
	Q10	相关系数	0.657^*	0.564^**	1.000	-0.079	0.022
		显著性(双尾)	0.000	0.000		0.608	0.885
		个案数	44	44	44	44	44
	Q13	相关系数	0.030	-0.098	-0.079	1.000	0.686^**
		显著性(双尾)	0.849	0.527	0.608		0.000
		个案数	44	44	44	44	44
	Q14	相关系数	0.045	-0.034	0.022	0.686^**	1.000
		显著性(双尾)	0.772	0.825	0.885	0.000	.
		个案数	44	44	44	44	44

表2

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