Learning from erroneous models using SCYDynamics

Dynamic phenomena are common in science education. Students can learn about such system dynamic processes through model based learning activities. This paper describes a study on the effects of a learning from erroneous models approach using the learning environment SCYDynamics. The study compared three conditions. Two experimental conditions where students had to correct errors in a model were contrasted to working with a correct model. The experimental conditions differed on whether or not the students had to detect the errors before correcting them. Results indicate that this approach enhanced students’ model testing and revising activities. Furthermore this approach was found to have a beneficial effect on learning common errors. Contrary to expectations this approach showed no learning effect on domain knowledge acquisition. The discussion further elaborates on improvements that might enhance this learning from erroneous model approac

Automating the analysis of problem-solving activities in learning environments: the co-lab case study

The analysis of problem-solving activities carried out by students in learning settings involves studying the students' actions and assessing the solutions they have created. This analysis constitutes an ideal starting point to support an automatic intervention in the student activity by means of feedback or other means to help students build their own knowledge. In this paper, we present a model-driven framework to facilitate the automation of this problemsolving analysis and of providing feedback. This framework includes a set of authoring tools that enable software developers to specify the analysis process and its intervention mechanisms by means of visual languages. The models specified in this way are computed by the framework in order to create technological support to automate the problem-solving analysis. The use of the framework is illustrated thanks to a case study in the field of System Dynamics where problem-solving practices are analysed.The Ministerio de Educación y Ciencia (España) has partially supported this research under Project TIN2011-29542-C02-02. The authors would like to express their gratitude to Ton de Jong, Wouter R. van Joolingen and Sylvia van Borkulo (University of Twente), for supporting this research. The work reported here was done during Rafael Duque’s stay at the Department of Instructional Technology of the University of Twente

AngeLA: Putting the Teacher in Control of Student Privacy in the Online Classroom

Learning analytics (LA) is often considered as a means to improve learning and learning environments by measuring student behaviour, analysing the tracked data and acting upon the results. The use of LA tools implies recording and processing of student activities conducted on software platforms. This paper proposes a flexible, contextual and intuitive way to provide the teacher with full control over student activity tracking in online learning environments. We call this approach AngeLA, inspired by an angel guarding over LA privacy. AngeLA mimics in a virtual space the privacy control mechanism that works well in a physical room: if a person is present in a room, she is able to observe all activities happening in the room. AngeLA serves two main purposes: (1) it increases the awareness of teachers about the activity tracking and (2) provides an intuitive way to manage the activity tracking permissions. This approach can be applied to various learning environments and social media platforms. We have implemented AngeLA in Graasp, a social platform that fosters collaborative activities

Scaffolding learning by modelling: The effects of partially worked-out models

Creating executable computer models is a potentially powerful approach to science learning. Learning by modelling is also challenging because students can easily get overwhelmed by the inherent complexities of the task. This study investigated whether offering partially worked-out models can facilitate students’ modelling practices and promote learning. Partially worked-out models were expected to aid model construction by revealing the overall structure of the model, and thus enabling student to create better models and learn from the experience. This assumption was tested in high school biology classes where students modelled the human glucose-insulin regulatory system. Students either received support in the form of a partial model that outlined the basic structure of the glucose-insulin system (PM condition; n = 26), an extended partial model that also contained a set of variables students could use to complete the model (PM+ condition; n = 21), or no support (control condition; n = 23). Results showed a significant knowledge increase from pretest to posttest in all conditions. Consistent with expectations, knowledge gains were higher in the two partial model conditions than in the control condition. Students in both partial model conditions also ran their model more often to check its accuracy, and eventually built better models than students from the control condition. Comparison between the PM and PM+ conditions showed that more extensive support further increased knowledge acquisition, model quality, and model testing activities. Based on these findings, it was concluded that partial solutions can support learning by modelling, and that offering both a structure of a model and a list of variables yields the best result