Educators in professional higher education experience difficulties addressing students’ self-awareness in their courses. The topic is often dismissed by students as ‘vague’ or ‘irrelevant’. This is detrimental for learning since knowing who you are and who you want to be is crucial: it guides behaviour and helps to feel balanced and in control. Support is needed to trigger students’ self-awareness and to make this process less demanding. In this paper we present guidelines for interactive triggers supporting students in developing their self-awareness. We asked students to discuss self-made photos in small groups and offered them three paper prototypes of triggers to work with. Questionnaire results and analysis of students’ discussions resulted in insights on how these triggers provoke distinct interaction and support self-awareness. Insights in advantages and disadvantages of the triggers can be used to design and implement innovative interactive systems that engage students in the process of developing self-awareness.

Deploying Learning Analytics that significantly improve learning outcomes remains a challenge. Motivation has been found to be related to academic achievement and is argued to play an essential role in efficient learning. We developed a Learning Analytics dashboard and designed an intervention that relies on goal orientation and social comparison. Subjects can see a prediction of their final grade in a course as well as how they perform in comparison to classmates with similar goal grades. Those with access to the dashboard ended up more motivated than those without access, outperformed their peers as the course progressed and achieved higher final grades. Our results indicate that learner-oriented dashboards are technically feasible and may have tangible benefits for learners.

This paper presents a case study of collaborative reflection. The reflection is anchored in a specially designed task related to several earlier students' assignments that dealt with the construction of scientific qualitative models using DynaLearn. Video analysis was used to explore the contributions of the task and the diagrammatic artifacts of the learning environment to the collaborative reflection. Evidence from additional data sources, further analysis and implications are discussed.

Conceptual modeling is a complex task that requires domain specific knowledge as well as a good command of modeling techniques. In this paper we propose an approach that aims to capture relevant knowledge from an online pool of conceptual models. This knowledge is brought to the user in order to assist the construction of new conceptual models. With our method, relevant feedback is generated based on knowledge extracted from the pool of models. Such feedback, tailored to the current modeling process of the user, allows the model to be improved based on shared knowledge.

Our long-term goal is to build a query answering system that can answer questions on a wide variety of topics and explain the answers. In such a situation, a designer faces the challenge of how to specify the KR&R requirements that are needed to answer questions. In this paper, we introduce a categorization of KR&R methods, and apply it to specifying the requirements for answering questions in six different domains: Physics, Chemistry, Biology, Environmental Science, Microeconomics, and U.S. Government & Politics. Drawing from the corpus of about 500 questions that we analyzed, we consider an example question in each domain and show the analytical process that we used to derive the requirements in terms of the KR&R categorization. We analyze the effectiveness of the current KR&R categorization, and identify directions for future work suggesting how this categorization can be further evolved by community participation.

Easy to use workbenches for Qualitative Reasoning (QR) and Modelling are virtually nonexistent. This has a limiting effect on the use and uptake of the technology by a larger audience. We present Garp3, a user-friendly workbench that allows modellers to build, simulate, and inspect qualitative models. Garp3 can be used to, discover, capture, and share conceptual knowledge on how systems behave.

Automated handling of tutoring and training functions in educational systems requires the availability of articulate domain models. In this article we further develop the application of qualitative models for this purpose. A framework is presented that defines a key role for qualitative models as interactive simulations of the subject matter. Within this framework our research focuses on automating the diagnosis of learner behaviour. We show how a qualitative simulation model of the subject matter can be reformulated to fit the requirements of general diagnostic engines such as GDE. It turns out that, due to the specific characteristics of such models, additional structuring is required to produce useful diagnostic results. A set of procedures is presented that automatically maps detailed simulation models into a hierarchy of aggregated models by hiding non-essential details and chunking chains of causal dependencies. The result is a highly structured subject matter model that enables the diagnosis of learner behaviour by means of an adapted version of the GDE algorithm. An experiment has been conducted that shows the viability of the approach taken, i.e., given the output of a qualitative simulator the procedures we have developed automatically generate a structured subject matter model and subsequently use this model to successfully diagnoses learner behaviour.