Zahn, Carmen
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Stressors in online exams – Same same but different?
2023-06, Roos, Anna-Lena, Jeitziner, Loris Tizian, Zahn, Carmen
What if the computer crashes? Findings from an exploratory factor analysis on stressors in online exams
2022-06, Jeitziner, Loris Tizian, Roos, Anna-Lena, Ruf, Alessia, Zahn, Carmen
The pandemic has forced higher education to shift from onsite to online environments. This novel situation may increase students’ exam stress and induce new stressors. In the present study, we identified stressors in online exams by conducting an exploratory factor analysis of a novel questionnaire. The analysis revealed five factors that categorize students’ experience of stress. Preliminary descriptive results suggest that possible system failures and social pressures cause the highest stress for students.
Logible: Detecting, Analyzing and Visualizing Behavior Sequences for Investigating Learning Behavior
2021, Ruf, Alessia, Jäger, Joscha, Niederhauser, Mario, Zahn, Carmen, Opwis, Klaus, Wichmann, Astrid, Hoppe, H. Ulrich, Rummel, Nikol
Logible is a highly sensitive web-based interactive tool that automatically detects behavior sequences from raw log files. It further analyzes and visualizes sequences and allows for comparisons of behavior data between different experimental conditions without the use of other software. Logible was developed based on an iterative, exploratory, and rule-based method devised to find meaningful sequences from 92 data sets of students who learned individually or collaboratively with an enhanced video-based environment. The tool is customizable and thus enables researchers to investigate learning behavior with various kinds of sequentially logged interaction data (from web-based video environments, online learning platforms etc.).
Exploring linguistic indicators of social collaborative group engagement
2023, Jeitziner, Loris Tizian, Paneth, Lisa, Rack, Oliver, Zahn, Carmen, Wulff, Dirk U., Damşa, Crina, Borge, Marcela, Koh, Elizabeth, Worsley, Marcelo
This study takes a NLP approach to measuring social engagement in CSCL-learning groups. Specifically, we develop linguistic markers to capture aspects of social engagement, namely sentiment, responsiveness and uniformity of participation and compare them to human ratings of social engagement. We observed small to moderate links between NLP-markers and human ratings that varied in size and direction across the different groups. We discuss measurement and prediction of social collaborative group engagement using natural language processing.
Introducing a new approach for investigating learning behavior
2021, Ruf, Alessia, Niederhauser, Mario, Jäger, Joscha, Zahn, Carmen, Opwis, Klaus, Hmelo-Silver, Cindy, de Wever, Bram, Oshima, Jun
The potential of learners’ video interactions to understand learning behavior has been recognized in previous research. However, little research has yet been conducted on enhanced video-based environments using behavior sequence analyses. Hence, we developed Logible, a sensitive, web-based tool to detect and analyze meaningful behavior sequences of learners interacting with such environments. The tool is based on an iterative method. With Logible we were able to visualize learning behavior and emphasize differences in experimental conditions.
A multi-method approach to capture quality of collaborative group engagement
2023, Paneth, Lisa, Jeitziner, Loris Tizian, Rack, Oliver, Zahn, Carmen, Damsa, Crina, Borge, Marcela, Koh, Elizabeth, Worsley, Marcelo
Multi-method approaches are an emerging trend in CSCL research as they allow to paint a more comprehensive picture of complex group learning processes than using a single method. In this contribution, we combined measures from different data sources to capture the quality of collaborative group engagement (QCGE) in CSCL-groups: QCGE-self-assessments, QCGE-ratings of verbal group communication, and video recorded nonverbal group behaviors. Using different methods of analysis, we visualized, described, and analyzed the data and related the measures to each other. Here, we present results suggesting that measures from different data sources are interrelated: For instance, nonverbal behavior (like nodding the head) is related to high QCGE-ratings of verbal communications. Results are preliminary and show disparities, too. Yet, we conclude that the multi-method approach results in a more comprehensive understanding of QCGE. Feasibility and suitability of the multi-method approach are discussed and conclusions for future research are drawn.
Impact of learners’ video interactions on learning success and cognitive load
2021, Ruf, Alessia, Leisner, David, Zahn, Carmen, Opwis, Klaus, Hmelo-Silver, Cindy, de Wever, Bram, Oshima, Jun
Enhanced video-based learning environments provide new tools (e.g., hyperlinks) – along with the well-known basic video control tools (e.g., play, pause, rewind) – that afford learners‘ enhanced interaction with videos. With these tools, learners can actively transform existing videos into their own hypervideo structures by adding hyperlinks and own materials. Unlike research on basic control tools that has revealed positive impacts on learning, research on enhanced tools is still rare and conflicting. It is thus open, whether the tools support generative interested learning or put too much extrinsic cognitive load onto learners. In the present study, we investigated the effects of video annotation and hyperlinking tools on learning success and cognitive load by analyzing tool-related interaction behavior data of 141 university students. Results indicated that the frequent use of enhanced video tools positively predicted learning success and a decrease in cognitive load. Implications of these results are discussed.