Christof Imhof- PhD
- Swiss Distance University of Applied Sciences
Christof Imhof
- PhD
- Swiss Distance University of Applied Sciences
About
17
Publications
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Introduction
Skills and Expertise
Current institution
Publications
Publications (17)
The development of self-directed learning (SDL) skills is crucial in today's rapidly changing environment, driven by digitalisation. Online learning offers opportunities to enhance SDL skills. Initially conceptualised by Malcolm S. Knowles for adult education, SDL involves diagnosing learning needs, setting goals, identifying resources, implementin...
Academic procrastination, i.e., the irrational delay of important academic
tasks, is a potentially harmful behaviour that is highly prevalent in higher
education (HE), especially distance education (DE). Given the wide range
of adverse outcomes, including mood, well-being and academic
performance, interventions might benefit many students. In this...
In this study, we used data from 44 undergraduate students from 14 classes to investigate the influence of the emotional states valence and arousal on attention and whether the addition of students' self-evaluation scores makes a difference. There is evidence in the literature that emotional states influence attention, which is defined as the focus...
The recent global pandemic has transformed the way education is delivered, increasing the importance of video-based online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-ba...
Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems and learning analytics, indicators of such behavior can be detected, enabling predictions of future procrast...
In this pilot study, we investigate emotions in students' texts by assessing valence and arousal. The goal is to find out whether students' texts that are mostly impersonal non-fiction contain emotional information and whether the form and the origin of the text differ regarding emotional information. Students on prolific will annotate 1000 randoml...
Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems and learning analytics, indicators of such behavior can be detected, enabling predictions of future procrastin...
Politics is emotional. So far, relatively few studies investigated the emotional content in parliamentary speeches. In this study, we analysed emotional valence and arousal of German and French speeches of a Swiss cantonal parliament and whether we can use them to predict the membership of parliamentarians to one of two groups: those who won more o...
Procrastination has been increasing since the proliferation of online learning. While traditionally assessed with self-report instruments, online learning offers the possibility to measure objective indicators (log data). In the present study, we aim to find out whether the combination of short scales on procrastination-related traits and log data...
Adaptive learning systems have been on the rise ever since the beginnings of e-learning. Their ability to provide instructions, guidance and content tailored towards learners’ individual needs bears a lot of potential for optimising learning processes. Due to recent technological advances, adaptive systems have become more sophisticated and prevale...
In this study, we examined how precisely a sentiment analysis and a word list-based lexical analysis predict the emotional valence (as positive or negative emotional states) of 63 emotional short stories. Both the sentiment analysis and the word list-based analysis predicted subjective valence, which however was predicted even more precisely when b...
This article demonstrates how an adaptive instructional design for a physics module can be realized in a standard learning management system. We implemented a didactic design with physics-specific online exercises that were accompanied by either detailed or non-detailed instructions, depending on the results of the previous task (or a prior knowled...
This study investigated the measurement of students’ emotional
states during a common learning activity, digital reading of
factual texts. The objective was to compare emotional selfreports
with automated facial emotion recognition. The latter
promises non-intrusive measurements of emotions, which could
inform adaptive learning systems. We used an...
The ongoing digitalisation facilitates measuring emotional characteristics of texts (e.g. lexical emotional valence), and emotional face expressions (e.g. facial emotional valence). In this context, a text was lexically analysed with the revised Berlin Af-fective Word List (BAWL-R), and videos of 91 subjects reading this text were analysed with a f...
Questions
Questions (2)
I've designed a questionnaire that not only has multiple dimensions, but also multiple criteria, in that every item is rated on multiple criteria, each with a separate rating scale. There are 6 dimensions, with 3 items each, all of which are rated on 4 criteria.
Now the question is how the reliability of such a questionnaire can be assessed. The analyses (internal consistency, factor analysis etc.) could theoretically be done separately for each criterion, but that of course ignores the relation between the criteria, which are all subcomponents of the overall construct. Is there a way to account for this data structure to its full extent?
In one of our eye-tracking studies, we displayed text within frames with varying sizes. The smaller, more narrow versions required the participants to scroll using a mouse, which was noted as a user event within BeGaze (a piece of eye-tracking analysis software by SMI). Our current issue is that we're not sure how to differentiate between two distinct types of mouse-click induced scrolling since participants could either scroll by clicking the arrows above and below the scroll bar or by dragging the bar itself. The third option, scrolling via the scroll wheel, was no issue since that's registered as a separate user event. Has anyone else encountered the same issue?
Thank you in advance!
