The ALGe-project is a cooperation project between the Chair of Educational Science, Jutta Mägdefrau, the professorship of History Education, Andreas Michler, and Andreas Gegenfurtner, assistant professor at the Department of Educational Development and Research at Maastricht University, the Netherlands.
Empirical research in humanities is still relatively rare in Germany. This piece of research examines the links between learning tasks, task perception, and learners’ characteristics. These connections should explain students’ task specific use of cognitive learning strategies and their achievement. It is expected that variables of task perception such as situational interest and task value could predict use of deep learning strategies. Students, who report a high level of deep learning strategies, are expected to outperform students using surface-level strategies.
The key question of this project is how to design learning tasks, which lead students to the use of deep learning strategies. We assume that applying deep-level strategies is a predictor of achievement in tasks which require historical reasoning. In addition to that, we are interested in task characteristics which could trigger situational interest or task value because these variables are known to be predictors of cognitive learning strategy use. Instruction-integrated task specific strategy related prompts should foster the probability that students use the prompted strategy.
Achievement data is generated through analyses of students’ essays requiring written historical reasoning. The essays are analyzed by using the SOLO-taxonomy (Biggs & Collis 1982). The objective of this project is to draw conclusions on how to design student-oriented individual instruction.
After conducting a pilot to test a new instrument for task-specific measurement of learning strategies, the main ALGe-project started in September of 2013 and ended in August of 2014. The sample covered 30 history classes in Bavaria comprising 801 students.