No abstract

The Community of Inquiry (CoI) Framework describes success factors for collaborative online-based learning. The CoI Survey is a validated instrument to measure these factors from the perspective of course participants. Until now, no validated translation of this Survey to German was available. The aim of this work was to translate the original English Survey to German and to validate the translated Survey instrument. After a systematic translation process, we validated the German translation in two higher education settings in two countries (entire data set of n=433 Surveys). By conducting item analysis, reliability analysis, exploratory factor analysis, and confirmatory factor analysis, we were able to confirm the reliability and validity of the German CoI Survey. Only one item (CP6) shows cross-loadings on two factors, a finding that was already discussed for the original CoI Survey. To conclude, the validated German version of the CoI Survey is now available.

Cognitive presence is a core construct of the Community of Inquiry (CoI) framework. It is considered crucial for deep and meaningful online-based learning. CoI-based real-time dashboards visualizing students’ cognitive presence may help instructors to monitor and support students’ learning progress. Such real-time classifiers are often based on the linguistic analysis of the content of posts made by students. It is unclear whether these classifiers could be improved by considering other learning traces, such as files attached to students’ posts. We aimed to develop a German-language cognitive presence classifier that includes linguistic analysis using the Linguistic Inquiry and Word Count (LIWC) tool and other learning traces based on 1,521 manually coded meaningful units from an online-based university course. As learning traces, we included not only the linguistic features from the LIWC tool, but also features such as attaching files to a post, tagging, or using terms from the course glossary. We used the k-nearest neighbor method, a random forest model, and a multilayer perceptron as classifiers. The results showed an accuracy of up to 82% and a Cohen’s κ of 0.76 for the cognitive presence classifier for German posts. Including learning traces did not improve the predictive ability. In conclusion, we developed an automatic classifier for German-language courses based on a linguistic analysis of students’ posts. This classifier is a step toward a teacher dashboard. Our work also provides the first fully CoI-coded German dataset for future research on cognitive presence.