Andreas Lachner

Mind the gap! Automated concept map feedback supports students in writing cohesive explanations.

Many students are challenged with the demand of writing cohesive explanations. To support students in writing cohesive explanations, we developed a computer-based feedback tool that visualizes cohesion deficits of students’ explanations in a concept map. We conducted three studies to investigate the effectiveness of such feedback as well as the underlying cognitive processes. In Study 1, we found that the concept map helped students identify potential cohesion gaps in their drafts and plan remedial revisions. In Study 2, students with concept map feedback conducted revisions that resulted in more locally and globally cohesive, and also more comprehensible, explanations than the explanations of students who revised without concept map feedback. In Study 3, we replicated the findings of Study 2 by and large. More importantly, students who had received concept map feedback on a training explanation 1 week later wrote a transfer explanation without feedback that was more cohesive than the explanation of students who had received no feedback on their training explanation. The automated concept map feedback appears to particularly support the evaluation phase of the revision process. Furthermore, the feedback enabled novice writers to acquire sustainable skills in writing cohesive explanations.

Model-Based Knowledge Mapping

One of the core tasks of knowledge management is knowledge assessment. Conducting knowledge assessment within organizations is considerably demanding because it is time and cost intensive and the results are often subjective and not very amenable to validation. In this chapter, we present a new theory-driven approach to knowledge management which simplifies this task considerably: model-based knowledge management. Furthermore, we introduce a new and fully automated text-based technology for the assessment of organizational knowledge. Within this technology new computer linguistic techniques and associative networks are used for knowledge mapping. The results of the technological study and our validation study indicate that our methodology is an efficient time- and cost-saving alternative to common knowledge mapping approaches.

Using Digital Knowledge Maps for Supporting Tutors Giving Effective Explanations

Explanations of expert tutors have shown to be superior compared to explanations of student tutors regarding students’ transfer of learning. However, there is little evidence which expertise-related features of explanations account for students’ processing and learning. In this chapter, we present a knowledge mapping approach for analyzing expertise-related features of explanations. Therefore, we used graph-oriented measures that take the explanatory structure into account. We conducted a series of experiments testing this knowledge mapping approach: First, our measures demonstrated to be able to detect expertise-related features of explanations, such as conciseness and coherence. Second, we could show that our measures both predicted students’ learning outcomes and learning processes, thus indicating the validity of our measures. Additionally, we discuss how our knowledge mapping approach could be used as a self-awareness tool for supporting tutors in giving coherent explanations. To increase the effectiveness of instructional explanations, our knowledge mapping approach could be a promising means for guiding tutors while writing instructional explanations.