Improving the quality of adaptive learning support provided by kindergarten teachers in play-based mathematical learning situations

Figures & data

Table 1. Study participants.

Participants	Country		Kindergarten teachers	Male
	Switzerland		65	0
	Germany		67	7
	Total		132	7
Years of professional experience	Country	Mean (SD)	Min.	Max.
	Switzerland	12.46 (9.43)	0	35
	Germany	14.61 (11.31)	1	41
	Total	13.54 (10.44)	0	41
Professional training	Country		Academic	Vocational
	Switzerland		31	34
	Germany		7	60
	Total		38	94

Figure 1. Study design.

Table 2. Rating instrument for assessing the quality of micro-adaptive learning support.

	Item	Indicator	Examples
Social-emotional learning support	Emotional warmth	• Non-verbal: benevolent gestures/mimicking; direct eye contact • Verbal: friendly and encouraging language • Responsiveness	• The kindergarten teacher uses encouraging nods and glances.
	Classroom management	• Presence of kindergarten teacher to maximize time on-task • Maintaining motivation by directing attention to the game • Preventing disturbances; appropriate regulation of disturbances	• The rules of the game are established and made explicit.
Subject-related learning support	Adaptive learning process stimulation	• Fit between the actions of the kindergarten teacher and the mathematical activities of the children • Promotion into the zone of proximal development through targeted explanations or hints, challenging questions, etc.	• How can you determine as quickly as possible the number of eyes on your dice?
	Stimulation of mathematical language	• Use of mathematical language by the kindergarten teacher • Demanding mathematical language from children	• How many more/fewer gold coins do you have than child X?

Ratings: 4 = clearly observable; 3 = mostly observable; 2 = partially observable; 1 = not observable. The maximum score for each item was 4, the minimum score 1.

Table 3. Rating instrument for assessingthe quality of macro-adaptive learning support.

Item	Indicator	Example interview questions
Planning of learning situation	• Use of professional knowledge to plan the game situation (game use, game allocation, group composition) in a targeted way.	• Why did you choose child X, Y and Z to play the games? • What was the reason for the group composition?
Diagnostic knowledge	• Explicit discussion of the mathematical competences of selected children in relation to the game.	• Which mathematical competence did you expect from each child?
Reflection	• Critical reflection on the goals and planning of the activity and learning support provided.	• At which points was it particularly important to support the children? • Which situations during the game were mathematically stimulating for the children?
Further learning support	• Explicit, relevant ideas about how to plan follow-up activities for individual children.	• What is important for the further mathematical support of each child?

Ratings: 4 = clearly observable; 3 = mostly observable; 2 = partially observable; 1 = not observable. The maximum score for each item was 4, the minimum score 1.

Table 4. Goodness-of-fit indicators for the CFA models.

Measurement point	factors	df	χ2	p	χ2/df	CFI	RMSEA	SRMR
1	3	16	28.791	.025	1.80	.960	.080	.056
2	3	16	29.396	.021	1.84	.952	.079	.056

Table 5. Mean, standard deviation, and analysis of variance in quality of learning support at t1.

Measures	group^MICRO		group^MACRO		Control group
	M	SD	M	SD	M	SD	F(2, 129)	η^2
Subject-related micro-adaptive support	4.47	1.15	4.47	1.29	4.22	1.11	0.608 ns	.009
Social-emotional micro-adaptive support	6.11	0.93	6.15	1.03	6.22	0.95	0.147 ns	.002
Macro-adaptive learning support	9.40	2.24	9.67	2.44	9.47	2.31	0.160 ns	.002

Table 6. Sequential regression analysis predicting the quality of subject-related micro-adaptive learning support at t2.

Step and predictor variable		R^2	Δ R^2	sr	B	SE B	β
Step 1		.06*	.06*
	Program^MICRO			.24	.59	.21	.24**
	Program^MACRO			.12	.30	.22	.12
Step 2		.24***	.18***
	Micro-adaptive support quality t1			.44	.39	.07	.42***
Step 3		.30***	.06*			.
	Country (0 = Germany; 1 = Switzerland)			-.16	-.35	.20	-.15
	Professional training (1  = acad.; 0 = vocational)			.24	.65	.24	.25 **
	Professional experience			-.03	-.003	.01	-.03

* p < .05. **p < .01. ***p < .001.

Table 7. Sequential regression analysis predicting the quality of macro-adaptive learning support at t2.

Step and predictor variable		R^2	Δ R^2	sr	B	SE B	β
Step 1		.05*	.05*
	Program^MICRO			0.02	.07	.40	.02
	Program^MACRO			0.25	1.18	.41	.25**
Step 2		.25***	.21***
	Macro-adaptive support quality t1			0.36	.35	.08	.36***
Step 3		.33***	.08**
	Country (0 = Germany; 1 = Switzerland)			0.14	.64	.40	.14
	Professional training (1 = academic; 0 = vocational)			−0.01	-.07	.46	-.02
	Professional experience			−0.26	-.05	.02	-.25**

* p < .05. **p < .01. ***p < .001.

Appendix A. Standard correlations between adaptive learning support quality at t2, professional development programs, adaptive learning support quality at t1, and control variables

	n	1	2	3	4	5	6	7	8	9	10	11
1. Subject-related micro-adaptive support t2	132	—
2. Social-emotional micro-adaptive support t2	132	.383**	—
3. Macro-adaptive support quality t2	132	.361**	.315**	—
4. Program^MICRO	132	.142	-.017	-.071	—
5. Program^MACRO	132	.013	.054	.226**	-.475**	—
6. Subject-related micro-adaptive support t1	132	.343**	.423**	.316**	.057	.006	—
7. Social-emotional micro-adaptive support t1	132	.226**	.616**	.263**	-.040	-.004	.656**	—
8. Macro-adaptive support quality t1	132	.247**	.331**	.460**	-.035	.048	.457**	.444**	—
9. Country	132	-.039	.519**	.344**	.027	.010	.385**	.511**	.449**	—
10. Professional training	132	.163	.253**	.224*	.031	-.068	.210*	.258**	.295**	.429**	—
11. Professional experience	132	-.078	-.087	-.247**	-.096	.181*	-.029	-.052	-.123	-.103	-.432**	—

Note. *** p < .001. **p < .01.* p < .05.