Effects of an early warning system on student absence and completion in Norwegian upper secondary schools: a cluster-randomised study

Figures & data

Figure 1. IKO Abridged theory of action.

Table 1. Annual activities and responsibilities of the IKO model.

Activity	When	Who
Analyse data in the software: identify at-risk students through information transferred from lower secondary school	Before school start	IKO responsible
Use the identification process and additional information from lower secondary schools to construct heterogeneous classes	Before school start	School leaders
Information to class teachers about students who have been identified as at-risk students through analyses of student data + additional information	Planning day/before school start	IKO responsible
Send out information about forthcoming interviews to students and parents/guardians	At school start	School leaders
Assessment A: Interviews with students identified by the software at school start	Within three weeks	Class teacher
Assessment B: Interviews with all students	Within four weeks	Class teacher
Assessment C: Evaluate the need for follow-up measures for students identified before school start (A) and additional students identified during interviews with class teachers (B)	Within four weeks	Teachers
Class teacher meetings to agree on and plan follow-up needs at class level	Within five weeks	Class teacher/IKO responsible
Mid-term evaluations for all students. Preliminary marks are given and registered in the software	1 November, 1 April, half-year evaluation	Teachers
Class teacher meetings to agree on follow-up needs at class level and develop follow-up interventions and recommendations to school leaders	1 November, 1 April, half-year evaluation	Class teacher/IKO responsible
Register students who need a closer follow-up	1 November, 1 April, half-year evaluation	Class teacher
Reflect on the results from mid-term evaluations. Evaluate the need for measures at individual, class, and/or school level. Evaluate the need for increasing competence in the staff	1 November, 1 April, half-year evaluation	Leaders/teachers

Table 2. Missing values in experimental and control conditions, % (n).

Variable	Experimental		Control		t-test
	%	(n)	%	(n)
Completion, 1st year	0.00	(0)	0.03	(1)	0.240
Completion, 2nd year	4.00	(178)	3.87	(125)	0.779
GPA, 1st year	4.00	(178)	3.69	(119)	0.484
GPA, 2nd year	10.11	(450)	9.24	(298)	0.201
Absence from lectures, 1st year	1.17	(52)	1.21	(39)	0.873
Absence from lectures, 2nd year	5.21	(232)	5.08	(164)	0.798
Days of absence, 1st year	1.15	(51)	1.21	(39)	0.801
Days of absence, 2nd year	5.19	(231)	5.15	(166)	0.928

Table 3. Characteristics by experimental and control condition.

Variable	Experimental	Control	t-test
Schools, N	20	22	n.a.
First-year students, N	4449	3229	n.a.
Number of students per school, M	222	147	0.025
Students in general study programmes, %	51.5	48.3	0.005
Completion of lower secondary, %	100	100	n.a.
Grade point average (GPA) lower secondary, M	4.06	4.02	0.072
Absence from lectures lower secondary, M	7.58	6.37	>0.001
Days of absence lower secondary, M	8.98	8.34	0.034

Note: All student characteristics represent the situation before entering upper secondary school (i.e., when leaving lower secondary).

Table 4. Student-level variables by experimental condition.

Variable	All students		At-risk group
	1st Year	2nd Year	1st Year	2nd Year
Grade point average (GPA), M
Control	3.90	3.94	3.41	3.53
Experimental	3.91	3.91	3.47	3.50
t-test	0.642	0.203	0.119	0.470
Design effect	5.687	5.211	4.172	3.664
Completion (%)
Control	86.6	85.3	72.5	71.7
Experimental	86.5	84.5	72.1	71.4
χ^2 test	0.945	0.381	0.795	0.868
Design effect	4.059	2.985	2.703	2.523
Absence from lectures, M
Control	10.7	14.5	17.3	19.3
Experimental	11.4	15.5	18.9	21.8
t-test	0.092	0.026	0.111	0.008
Design effect	4.743	3.796	3.319	2.306
Days of absence, M
Control	5.2	5.7	8.7	8.9
Experimental	5.3	5.8	9.0	9.0
t-test	0.446	0.524	0.474	0.758
Design effect	2.038	2.205	1.720	1.605

Table 5. Effect of the IKO model. All students.

	Completion (1)		GPA (2)		Absence from lectures (3)		Days of absence (4)
	1st year	2nd year	1st year	2nd year	1st year	2nd year	1st year	2nd year
	OR	OR	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
IKO model	1.144	1.066	0.005	0.021	−0.325	−0.631	0.094	−0.190
Standard errors	0.170	0.133	0.033	0.032	0.745	0.773	0.313	0.339
P-value	0.364	0.607	0.875	0.502	0.662	0.414	0.765	0.575
N of observations:	7,552	7,255	7,173	6,741	7,461	7,160	7,461	7,164
N of schools:	42	42	42	42	42	42	42	42
ICC	0.033	0.020	0.025	0.014	0.011	0.008	0.008	0.008

Note: All models control for educational programme, school size, baseline absence (days and lectures) and the outcome measurement at baseline. Second-year models also control for students’ change of school.

Table 6. Effect of the IKO model. At-risk students.

	Completion (1)		GPA (2)		Absence from lectures (3)		Days of absence (4)
	1st year	2nd year	1st year	2nd year	1st year	2nd year	1st year	2nd year
	OR	OR	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
IKO model	1.090	1.038	0.003	−0.014	−0.269	0.664	−0.012	−0.788
Standard errors	0.189	0.170	0.043	0.048	1.683	1.521	0.644	0.650
p-value	0.621	0.822	0.945	0.773	0.873	0.662	0.985	0.225
N of observations:	2,618	2,485	2,331	2,152	2,578	2,449	2,579	2,451
N of schools:	42	42	42	42	42	42	42	42
ICC	0.004	0.003	0.011	0.007	0.021	0.012	0.008	0.005

Note: All models control for educational programme, school size, baseline absence (days and lectures) and the outcome measurement at baseline. Second-year models also control for students’ change of school.