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This paper explored the effects of in-class-level differentiation by making innovative use of an interactive whiteboard (SMARTboard) on math proficiency. Therefore, this paper evaluates the use of SMARTboard in class, in combination with teacher training, using a randomized field experiment among 199 pre-vocational students in seventh grade in the Netherlands. During 6 weeks, students in the intervention group participated in math classes in which the SMARTboard was used to apply level differentiation. The teachers of these classes received a specific training (Technological Pedagogical and Content Knowledge [TPACK]) in using the SMARTboard in class. Control classes were taught by teachers without the training, who did not use the SMARTboard in class. The results showed that level differentiation in class, which was possible because of the efficient use of the SMARTboard, significantly increased math proficiency with 0.25 points.

This paper describes a dataset with data from three individually randomized educational technology experiments on differentiation, formative testing and feedback during one school year for a group of 8th grade students in the Netherlands, using administrative data and the online motivation questionnaire of Boekaerts. The dataset consists of pre- and posttest scores for each experiment, as well as background information on the students, such as gender, age, ability, grade repetition and neighborhood characteristics. It also includes motivation of students. The dataset, or parts of it, could be interesting to use in future studies, focusing on effects of the use of educational technology, for comparison with similar and/or slightly different experiments, and for studies such as meta-analyses or replication studies, especially given the individual experimental setup.

This paper describes a dataset with data from three individually randomized educational technology experiments on differentiation, formative testing and feedback during one school year for a group of 8th grade students in the Netherlands, using administrative data and the online motivation questionnaire of Boekaerts. The dataset consists of pre- and posttest scores for each experiment, as well as background information on the students, such as gender, age, ability, grade repetition and neighborhood characteristics. It also includes motivation of students. The dataset, or parts of it, could be interesting to use in future studies, focusing on effects of the use of educational technology, for comparison with similar and/or slightly different experiments, and for studies such as meta-analyses or replication studies, especially given the individual experimental setup. [ABSTRACT FROM AUTHOR]

The GOALS study ( Grootschalig Onderzoek naar Activiteiten van Limburgse Scholieren [ Large-scale Research of Activities in Dutch Students]) was set up to investigate possible associations between different forms of physical activity and inactivity with cognitive performance, academic achievement and mental well-being. It was conducted at a secondary school in the south of the Netherlands. Data collection took place during normal school weeks from October 2011 to March 2012, and 1 year later from October 2012 to May 2013. The study was approved by the local Ethical Committee of the Open University of the Netherlands. All students in grades 7 and 9 of senior general secondary education and university preparatory education were invited to participate ( n = 526). At baseline, a sample of 440 Dutch-speaking adolescents was willing to participate (83.7%). Of these, 344 participated in the follow-up. Physical activity was measured objectively by an Activ Pal3TM accelerometer (Paltechnologies, Glasgow, UK). Cognitive performance was measured by the d2 Test of Attention and the Symbol Digit Modalities Test. Academic achievement was determined using the mean of school grades in Dutch, English and Mathematics. Several well-validated instruments were used to determine mood/depressive symptoms, self-esteem and goal orientation. Information on several covariates such as sex, academic year, school level, ethnicity, socio-economic status, body mass index, pubertal status, smoking, drinking, viewing television, computer use, etc., are available. [ABSTRACT FROM AUTHOR]