Does the Digital Tools-supported Teaching Have an Impact on Students’ Learning Results:Meta-Analysis Evidence from 137 Experiments and Quasi Experiments

doi:10.16382/j.cnki.1000-5560.2022.11.008

Abstract

Abstract:

In order to clarify the impact of digital tools-supported teaching on students’ learning outcomes, this study conducted a meta-analysis based on 137 experiments and quasi-experiments. The results show that compared with traditional teaching, digital tools-supported teaching can enhance learning motivation (g=0.482) and improve academic achievement (g=0.479). In addition, this positive effect was moderated by demographics, knowledge assessments, and study attributes.The specific manifestations were as follows: in terms of demographic, the use of digital tools to support teaching in the context of collectivism culture had a more significant positive impact on learning motivation and academic achievement compared with individualism culture; the predictive effect of digital tools-supported teaching on learning motivation and academic achievement is not affected by the stage; the influence of digital tools-supported teaching on boys’ academic achievement is significantly higher than that of girls. In terms of knowledge assessment, compared with digital tutorials and simulation, intelligent tutoring system and hypermedia are more conducive to improving academic achievement; as opposed to other subjects, the teaching supported by digital tools has a greater impact on language learning motivation, but the teaching supported by digital tools has a significant impact on mathematics and science achievement. In terms of study attributes, the use of digital tools by trained teachers had a better impact on student motivation than did untrained teachers; only when the intervention time of digital teaching is moderate can it give full play to its best effect on academic achievement; the impact of digital tools-supported teaching on academic achievement is strengthened as the intensity of intervention increases; with the change of years, the predictive effect of digital tools- supported teaching on learning motivation will become more and more prominent.

Key words: digital tools, teaching, learning motivation, academic achievement, meta-analysis

Hao Lei, Xue Li. Does the Digital Tools-supported Teaching Have an Impact on Students’ Learning Results:Meta-Analysis Evidence from 137 Experiments and Quasi Experiments[J]. Journal of East China Normal University(Educational Sciences), 2022, 40(11): 92-109.

Figures/Tables 7

Moderator variables	Between groups effect(Q_BET)	k	g	SE	95%CI for g	Homogeneity test within each group(Q_W)	I²
学习动机
文化背景	*10.054（p=0.015）**
集体主义		41	0.643	0.100	[0.448，0.838]	243.576***	0.84
个人主义		12	?0.059	0.198	[?0.446，0.329]	54.732	0.80
样本学段	1.422（p=0.491）
小学		19	0.601	0.141	[0.326，0.877]	119.784***	0.85
初中		16	0.536	0.118	[0.305，0.767]	71.297***	0.77
高中		18	0.285	0.227	[?0.161，0.730]	216.077	0.91
工具类型	2.038（p=0.565）
数字教程		18	0.416	0.191	[0.042，0.790]	139.147*	0.88
智能辅导系统		7	0.751	0.283	[0.197，1.306]	91.984***	0.93
模拟应用程序		21	0.381	0.075	[0.235，0.528]	38.752***	0.48
超媒体		7	0.714	0.463	[?0.193，1.621]	97.861	0.94
学科领域	*8.810（p=0.032）**
语言		9	0.884	0.215	[0.463，1.305]	55.954***	0.86
数学		17	0.355	0.098	[0.163，0.547]	41.583***	0.62
科学		22	0.467	0.204	[0.068，0.866]	228.150*	0.91
其他		5	0.270	0.043	[0.185，0.355]	1.448***	0.00
教师培训	*4.440（p=0.035）**
是		18	0.841	0.179	[0.490，1.192]	194.982***	0.91
否		16	0.253	0.214	[?0.166，0.672]	131.429	0.89
干预时长	2.649（p=0.449）
$\leqslant $ 4周		10	0.478	0.161	[0.162，0.795]	70.728**	0.87
>4， $\leqslant $ 12周		11	0.973	0.285	[0.414，1.533]	108.799***	0.91
>12， $\leqslant $ 24周		3	0.435	0.235	[?0.026，0.895]	11.403	0.82
>24周		3	0.503	0.544	[?0.564，1.570]	25.691	0.92
干预强度	0.777（p=0.678）
<1h/周		4	0.599	0.265	[0.081，1.118]	14.552***	0.79
$\geqslant $ 1h，<3h/周		4	0.857	0.461	[?0.047，1.762]	53.834***	0.94
$\geqslant $ 3h/周		5	0.410	0.255	[?0.090，0.911]	33.164***	0.88
学业成绩
文化背景	*3.994（p=0.046）**
集体主义		86	0.525	0.043	[0.441，0.609]	286.696***	0.70
个人主义		27	0.315	0.096	[0.126，0.503]	190.713**	0.86
样本学段	*6.149（p=0.046）**
小学		38	0.355	0.056	[0.246，0.464]	88.651***	0.58
初中		43	0.574	0.073	[0.430，0.717]	238.667***	0.82
高中		34	0.496	0.076	[0.348，0.645]	156.544***	0.79
工具类型	*10.336（p=0.016）**
数字教程		60	0.372	0.057	[0.260，0.484]	260.429***	0.77
智能辅导系统		12	0.745	0.135	[0.481，1.009]	56.436***	0.81
模拟应用程序		37	0.533	0.060	[0.414，0.651]	98.326***	0.63
超媒体		6	0.697	0.148	[0.407，0.986]	25.132***	0.80
学科领域	*7.851（p=0.049）**
语言		30	0.338	0.085	[0.171，0.504]	156.591***	0.81
数学		37	0.470	0.072	[0.328，0.612]	156.002***	0.77
科学		43	0.579	0.062	[0.457，0.701]	152.491***	0.72
其他		4	0.627	0.079	[0.473，0.781]	1.345***	0.00
教师培训	0.136（p=0.712）
是		32	0.408	0.063	[0.286，0.531]	128.135***	0.76
否		50	0.443	0.070	[0.306，0.580]	264.759***	0.81
干预时长	16.744（p<0.001）**
$\leqslant $ 4周		16	0.543	0.118	[0.312，0.774]	68.738***	0.78
>4， $\leqslant $ 12周		22	0.502	0.108	[0.289，0.714]	100.210***	0.79
>12， $\leqslant $ 24周		8	0.759	0.147	[0.470，1.047]	27.166***	0.74
>24周		12	0.255	0.045	[0.166，0.344]	7.838***	0.00
干预强度	19.950(P<0.001)*
<1h/周		3	0.207	0.097	[0.016，0.398]	1.987*	0.00
$\geqslant $ 1h，<3h/周		15	0.349	0.139	[0.077，0.620]	111.032*	0.87
$\geqslant $ 3h/周		13	0.794	0.094	[0.610，0.978]	27.133***	0.56

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	k	g	95%CI	t	Level1 (%)	Level2 (%)	Level3 (%)	Homogeneity test
	k	g	95%CI	t	Level1 (%)	Level2 (%)	Level3 (%)	Q	p	I²(%)
学习动机	53	0.482	[0.286，0.678]	4.93***	7.54	0	92.46	381.25	<0.0001	86.4
学业成绩	115	0.479	[0.399，0.560]	11.82***	20.81	4.59	74.60	505.07	<0.0001	77.4

	intercept	95%CI	t	p	Nfs
学习动机	1.113	[?0.53，?2.76]	1.325	0.19	3525
学业成绩	1.073	[?0.21，?2.36]	1.635	0.10	8282

Parameter	Estimate	SE	T-value	P-value	95%CI for b	Q_E	I²
学习动机
女性（%）	Q_Model(1，k=17)=1.185，p=0.292
β₀	0.560	0.150	3.721	0.002	[0.242，0.877]	142.869***	0.90
β₁	0.923	0.847	1.089	0.292	[?0.865，2.710]	142.869***	0.90
男性（%）	Q_Model(1，k=17)=1.185，p=0.292
β₀	0.560	0.150	3.721	0.002	[0.242，0.877]	142.869***	0.90
β₁	?0.923	0.847	1.089	0.292	[?2.710，0.865]	142.869***	0.90
出版年份	Q_Model(1，k=51)=4.786*，p=0.033
β₀	0.485	0.094	5.150	<0.001	[0.296，0.674]	340.890***	0.92
β₁	0.038	0.018	2.188	0.033	[0.031，0.073]	340.890***	0.92
学业成绩
女性（%）	Q_Model(1，k=40)=10.097**，p=0.003
β₀	0.385	0.057	6.780	<0.001	[0.270，0.500]	118.282***	0.73
β₁	?1.290	0.406	?3.178	0.003	[?2.111，?0.470]	118.282***	0.73
男性（%）	Q_Model(1，k=40)=10.097**，p=0.003
β₀	0.385	0.057	6.780	<0.001	[0.270，0.500]	118.282***	0.73
β₁	1.290	0.406	?3.178	0.003	[0.470，2.111]	118.282***	0.73
出版年份	Q_Model(1，k=113)=0.006，p=0.937
β₀	0.480	0.041	11.769	<0.001	[0.399，0.560]	505.072***	0.80
β₁	0.001	0.008	0.079	0.937	[?0.016，0.017]	505.072***	0.80

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