Examining the landscape of teacher learning for data use: The case of Illinois

Figures & data

Table 1. Sample descriptive statistics for teacher data use practices (N = 329)

Item	M	SD
Determine students’ level of achievement after instruction	3.72	1.05
Identify next steps for instruction (e.g. move on and/or re-teach)	3.66	1.14
Identity patterns in student thinking (e.g. errors and/or misconceptions)	3.64	1.20
Evaluate the effectiveness of your instruction (e.g. lessons and/or units)	3.63	1.11
Modify instruction or lesson plans for current students (e.g. activities, representations, and/or materials)	3.60	1.12
Identify student’s strengths and weaknesses	3.53	1.11
Give feedback to students	3.50	1.13
Plan/design lessons	3.48	1.12
Differentiate instruction	3.47	1.15
Select appropriate instructional strategies	3.46	1.16
Assign grades	3.45	1.33
Monitor students’ achievement growth/progress over time	3.43	1.10
Identify student learning needs	3.43	1.14
Modify instruction or lessons plans for future students (e.g. activities, representations, and/or materials)	3.33	1.13
Identify gaps in student knowledge	3.28	1.10
Select appropriate supplemental interventions	3.25	1.16
Determine students’ level of achievement before instruction	3.15	1.11
Identify reasons for poor student performance	3.14	1.09
Select scaffolds to provide	3.05	1.22
Identify students for individualized instruction	3.05	1.17
Set student performance goals or targets	2.95	1.12
Group students (either homogeneously or heterogeneously)	2.92	1.17
Communicate student’s performance to parents	2.85	1.09
Identify students for more intensive intervention	2.83	1.10
Select which content to teach	2.70	1.20
Select assessments to administer	2.68	1.12
Identify students for acceleration/enrichment	2.61	1.10

Notes: Response format was: 1 = never, 2 = once a month or less, 3 = a few times per month, 4 = once a week, and 5 = a few times per week. Items are sorted by mean. M = mean. SD = standard deviation.

Table 2. Sample response distributions for undergraduate and graduate coursework and in-school learning opportunities related to data use (percentages)

	0 courses	1 course	2 courses	3 or more courses	Any coursework
Undergraduate coursework
Assessment	37.3	34.8	18.8	9.1	62.7
Data-driven decision-making/data use	63.1	22.7	10.1	4.1	36.9
Response to intervention/progress monitoring	63.4	21.6	10.0	5.0	36.6
Teacher inquiry/teacher research/action research	51.1	28.8	11.9	8.2	48.9
Graduate coursework
Assessment	37.2	32.5	20.6	9.7	62.8
Data-driven decision-making/data use	50.5	27.6	14.1	7.8	49.5
Response to intervention/progress monitoring	56.2	25.8	13.0	5.0	43.8
Teacher inquiry/teacher research/action research	39.1	31.7	17.4	11.8	60.9
In-school activities	No	Yes	–	–	–
In-service workshop(s) about assessment	10.9	89.1	–	–	–
In-service workshop(s) about data-driven decision-making/data use	23.0	77.0	–	–	–
Data teams	37.3	62.7	–	–	–
Data coaching	75.9	24.1	–	–	–
Professional learning communities focused on assessment/data	32.0	68.0	–	–	–

Notes: Any coursework figures are the sum of percentages of respondents endorsing 1 course, 2 courses, or 3 or more courses, and may not sum to 100 due to rounding. Survey question Ns ranged from 317 to 322.

Table 3. Descriptive statistics for multiple regression analytic sample (N = 296)

Variable	M	SD
Data use practices	−.01	.99
City locale	.07	.26
Town locale	.09	.29
Rural locale	.13	.34
Pre-K level	.01	.10
Middle level	.40	.49
High level	.23	.42
Other level	.04	.19
Leadership	3.57	.76
Assessment improves teaching belief	4.01	.97
Self-efficacy for application of data to instruction belief	3.82	.73
Undergraduate data use/DBDM course	.35	.48
Graduate RtI/progress monitoring course	.42	.49
Graduate assessment course	.62	.49

Notes: DBDM = data-based decision-making. RtI = response to intervention. M = mean. SD = standard deviation. Data use practices, leadership, assessment improves teaching belief, and self-efficacy for application of data to instruction belief variables were continuous, whereas all other variables were dichotomous (dummy variables).

Table 4. Successive multiple regression analysis model’s results and coefficients (N = 296)

Regressor variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Block 1	–	–	–	–	–	–
City locale	.32	.28	.20	.21	.17	.21
Town locale	−.29	−.33	−.26	−.23	−.22	−.23
Rural locale	−.22	−.26	−.28	−.18	−.17	−.22
Pre-K level	.23	.12	.20	−.10	−.13	−.15
Middle level	−.34*	−.31*	−.27*	−.23	−.23	−.23
High level	−.15	−.08	−.06	−.05	−.06	−.06
Other level	.12	.08	.05	.12	.14	.14
Leadership	.20**	.07	−.02	−.05	−.05	−.06
Block 2	–	–	–	–	–	–
Assessment improves teaching belief	–	.38***	.28***	.26***	.27***	.28***
Block 3	–	–	–	–	–	–
Self-efficacy for application of data to instruction belief	–	–	.31***	.29***	.28***	.29***
Block 4	–	–	–	–	–	–
Undergraduate data use/DBDM course	–	–	–	.45***	.40***	.41***
Graduate RtI/progress monitoring course	–	–	–	–	.23*	.34**
Graduate assessment course	–	–	–	–	–	−.28*
Model F	3.09**	8.36***	10.99***	12.17***	11.75***	11.54***
R^2	.08	.21	.28	.32	.33	.35
$R_{\text{A}}^2$	.05	.18	.25	.29	.30	.32
ΔR^2	–	.13	.07	.04	.01	.02
ΔF	–	46.66***	27.59***	17.62***	5.12*	6.36*

Notes: DBDM = data-based decision-making. RtI = response to intervention. Model coefficients for continuous variables are standardized, but unstandardized for dummy variables; the dependent variable was standardized prior to analysis.

* p < .05.

** p < .01.

*** p < .001.