Acceptance of artifcial intelligence among pre-service teachers: a multigroup analysis - Zhang, Chengming; Schießl, Jessica; Plößl, Lea; Hofmann, Florian; Gläser-Zikuda, Michaela (International Journal of Educational Technology in Higher Education, 2023)
Over the past few years, there has been a significant increase in the utilization of artificial intelligence (AI)-based educational applications in education. As pre-service teachers’ attitudes towards educational technology that utilizes AI have a potential impact on the learning outcomes of their future students, it is essential to know more about pre-service teachers’ acceptance of AI. The aims of this study are (1) to discover what factors determine pre-service teachers’ intentions to utilize AI-based educational applications and (2) to determine whether gender differences exist within determinants that affect those behavioral intentions. A sample of 452 pre-service teachers (325 female) participated in a survey at one German university. Based on a prominent technology acceptance model, structural equation modeling, measurement invariance, and multigroup analysis were carried out. The results demonstrated that eight out of nine hypotheses were supported; perceived ease of use (β = 0.297***) and perceived usefulness (β = 0.501***) were identified as primary factors predicting pre-service teachers’ intention to use AI. Furthermore, the latent mean differences results indicated that two constructs, AI anxiety (z = − 3.217**) and perceived enjoyment (z = 2.556*), were significantly different by gender. In addition, it is noteworthy that the paths from AI anxiety to perceived ease of use (p = 0.018*) and from perceived ease of use to perceived usefulness (p = 0.002**) are moderated by gender. This study confirms the determinants influencing the behavioral intention based on the Technology Acceptance Model 3 of German pre-service teachers to use AI-based applications in education. Furthermore, the results demonstrate how essential it is to address gender-specific aspects in teacher education because there is a high percentage of female pre-service teachers, in general. This study contributes to state of the art in AI-powered education and teacher education.<br
Keywords: Artifcial intelligence; Educational technology; Acceptance; Pre-service teachers

AI-powered automatic feedback on reflective writing - Zhang, Chengming; Schießl, Jessica; Plößl, Lea; Hofmann, Florian; Gläser-Zikuda, Michaela; Solopova, Veronika; Romeike, Ralf; Benzmüller, Christoph; Landgraf, Tim; Witte, Sascha (European Teacher Education Network (ETEN) “Teacher Education – Connecting Glocal”, 2023)

Akzeptanz von künstlicher Intelligenz (KI) bei angehenden Lehrkräften – eine Frage des Geschlechts? - Zhang, Chengming; Schießl, Jessica; Plößl, Lea; Hofmann, Florian; Gläser-Zikuda, Michaela (Sektionstagung der Arbeitsgruppe empirische pädagogische Forschung (AEPF) in der Deutschen Gesellschaft für Erziehungswissenschaft (DGfE), 2022)
Angesichts der zunehmenden Anwendung KI im Bildungsbereich (Pedro et al., 2019) stellt die Vorbereitung künftiger Lehrkräfte auf KI-gestützte Bildungsangebote eine besondere Herausforderung dar. Vor diesem Hintergrund wurde in dieser Studie ermittelt, welche Faktoren die Absicht zur Nutzung von KI bestimmen, und ob geschlechtsspezifische Unterschiede diese Faktoren aus der Sicht von angehenden Lehrkräften auf der Grundlage des Technology Acceptance Model 3 (Venkatesh & Bala, 2008) beeinflussen. 452 Lehramtsstudierende wurden mit einem online-Instrument an einer deutschen Universität befragt. In der Studie wurde ein an das TAM3 mittels Strukturgleichungsmodellierung, Messinvarianz und Mehrgruppenanalyse geprüft. Die Ergebnisse zeigen, dass acht von neun Hypothesen bestätigt werden konnten; die wahrgenommene Benutzerfreundlichkeit und die wahrgenommene Nützlichkeit wurden als zwei primäre Faktoren bestätigt. Auffallend ist außerdem, dass die Pfade der KI-Angst zur wahrgenommenen Benutzerfreundlichkeit und der wahrgenommenen Benutzerfreundlichkeit zur wahrgenommenen Nützlichkeit durch das Geschlecht beeinflusst werden. Limitationen und Implikationen der Studie werden diskutiert.<br
Keywords: KI; KI-Akzeptanz; Technologie-Akzeptanz; TAM; Lehrerinnenbildung; Lehrerbildung; Geschlechterspezifische Unterschiede; Gender

Classification of reflective writing: A comparative analysis with shallow machine learning and pre-trained language models - Zhang, Chengming; Hofmann, Florian; Plößl, Lea; Gläser-Zikuda, Michaela (Education and Information Technologies, 2024)
Reflective practice holds critical importance, for example, in higher education and teacher education, yet promoting students’ reflective skills has been a persistent challenge. The emergence of revolutionary artificial intelligence technologies, notably in machine learning and large language models, heralds potential breakthroughs in this domain. The current research on analyzing reflective writing hinges on sentence-level classification. Such an approach, however, may fall short of providing a holistic grasp of written reflection. Therefore, this study employs shallow machine learning algorithms and pre-trained language models, namely BERT, RoBERTa, BigBird, and Longformer, with the intention of enhancing the document-level classification accuracy of reflective writings. A dataset of 1,043 reflective writings was collected in a teacher education program at a German university (M = 251.38 words, SD = 143.08 words). Our findings indicated that BigBird and Longformer models significantly outperformed BERT and RoBERTa, achieving classification accuracies of 76.26% and 77.22%, respectively, with less than 60% accuracy observed in shallow machine learning models. The outcomes of this study contribute to refining document-level classification of reflective writings and have implications for augmenting automated feedback mechanisms in teacher education.
CRIS-Team Scopus Importer:2024-05-10
Keywords: AI feedback; Pre-trained language model; Reflective writing; Shallow machine learning; Teacher education

Comparative analysis of pre-service teachers’ reflective writing: using shallow machine learning and pre-trained language models - Zhang, Chengming; Hofmann, Florian; Plößl, Lea; Gläser-Zikuda, Michaela (Tag der Forschung 2023, 2023)

Das BMBF-Projekt PetraKIP – KI-Einsatz in der Hochschullehre: Ein Werkstattbericht. - Hofmann, Florian; Zhang, Chengming (DigiLLAB Inspirationstag „KI in Schule und Lehrkräftebildung – Die Kunst der Intelligenz“, 2023)

Evaluating Reflective Writing in Pre-Service Teachers: The Potential of a Mixed-Methods Approach - Zhang, Chengming; Schießl, Jessica; Plößl, Lea; Hofmann, Florian; Gläser-Zikuda, Michaela (Education Sciences, 2023)
Reflective writing is a relevant aspect of pre-service teachers’ professionalization. Evaluating reflective writing in teacher education is demanding due to a shortage of resources. Hence, this study explores the practical possibilities of evaluating reflective writing using a mixed-methods approach to analyze reflective writing from 198 pre-service teachers at a German university. We used qualitative content analysis, computational linguistic approaches, and BERTopic. Results of qualitative content analysis results indicated primarily descriptive and low-level participants’ reflective writing. Next, computational linguistic analyses revealed that affective and cognitive terminology utilization differed across varying levels of reflection, with a higher frequency of such terms correlating with deeper levels of reflection. BERTopic results showed that reflective content mainly centered on learning materials and shifted toward affective and motivational themes related to higher levels of reflection. This study demonstrates that reflective writing can be evaluated across reflection levels and cognitive, affective, and thematic dimensions, combining qualitative content analysis, computational linguistic approaches, and BERTopic.<br
Keywords: reflective writing; qualitative content analysis; computational linguistics; BERTopic

Förderung der Reflexionsfähigkeit von Lehramtsstudierenden: Zum Potential von KI-basiertem Feedback - Hofmann, Florian; Zhang, Chengming; Solopova, Veronika; Plößl, Lea; Gläser-Zikuda, Michaela (AEPF-Tagung „Wissenstransfer zwischen Bildungsforschung, Bildungspraxis und Lehrer*innenbildung", Universität Osnabrück., 2024)
Förderung der Reflexionsfähigkeit von Lehramtsstudierenden: Zum Potential von KI-basiertem Feedback Florian Hofmann1, Chengming Zhang1, Veronika Solopova2, Lea Plößl1, Michaela Gläser-Zikuda1 1Universität Erlangen-Nürnberg; 2Universität Bamberg Reflexionsfähigkeit gilt in der Lehrkräftebildung als entscheidender Professionalisierungsaspekt, stellt aber insbesondere für angehende Lehrkräfte eine Herausforderung dar. Feedback hat sich im Lehr-Lernkontext, auch in Bezug auf reflexives Schreibens, als wirksam erwiesen. Ziel dieser quasi-experimentellen Studie war es das Potential von KI-generiertem Feedback im Vergleich zu Rückmeldungen durch Dozierende für die Entwicklung von Reflexionsfähigkeit zu prüfen. Insgesamt 72 randomisiert ausgewählte Lehramtsstudierende (76.34 % weiblich, M = 2.33 Semester) verfassten zwei schriftliche Reflexionen. Die Studierenden erhielten basierend auf denselben Kriterien ein schriftliches Feedback entweder durch Dozierende, ChatGPT oder einen selbst entwickelten KI-Chatbot. Die Reflexionstexte wurden theoriebasiert bewertet. Dabei wurden die Reflexionstexte der ChatGPT-Feedback-Gruppe am besten bewertet, gefolgt von der Dozierenden- und schließlich der Chatbot-Feedback-Gruppe (F (2,69) = 6.22**, η² = 0.15). Es zeigten sich keine sign. Unterschiede zwischen den Rückmeldeformaten in Bezug auf die Reflexionsfähigkeit. Limitationen der Studie und Implikationen für die Lehre werden diskutiert.
Keywords: Reflexionsfähigkeit; Reflexionskompetenz; KI; Feedback; Lehrer*innenbildung; ChatGPT; Hochschulbildung

Mixed Methods Research on Reflective Writing in Teacher Education - Gläser-Zikuda, Michaela; Zhang, Chengming; Hofmann, Florian; Plößl, Lea; Pösse, Lisa; Artmann, Michaela (Frontiers in Psychology, 2024)
In this paper, mixed methods research is presented and discussed in the context of research on reflective writing for the professional development of pre-service teachers. First, we present prominent theoretical frameworks to analyze reflective writings. Second, we review relevant methodological approaches of research on reflective writings in teacher education, such as qualitative, quantitative and mixed methods. Third, we present a study from our research lab combining qualitative content analysis and linguistic analyses as an example for a concurrent mixed method approach. The results of the qualitative content analysis indicated that 198the reflective writingsons of 198 pre-service teachers were primarily descriptive and on a low level. Computational linguistic analyses revealed that affective and cognitive terminology utilization in reflective writing differed significantly across the different levels of reflection, with a higher frequency of such terms correlating with deeper levels of reflection. Thus, essential challenges and opportunities of implementing such a mixed method study to analyze reflective writings are illustrated and discussed. Finally, we conclude the paper by discussing on how mixed methods approaches might be further advanced in the field of reflective writing research in teacher educatio
Keywords: mixed methods; Qualitative content analysis; computational linguistics; reflective writing; Teacher Education

PapagAI: Automated Feedback for Reflective Essays - Solopova, Veronika; Rostom, Eiad; Cremer, Fritz; Gruszczynski, Adrian; Witte, Sascha; Zhang, Chengming; Ramos L´opez, Fernando; Plößl, Lea; Hofmann, Florian; Gläser-Zikuda, Michaela; Romeike, Ralf; Benzmüller, Christoph; et al. (KI 2023 – 46th German Conference on Artificial Intelligence, 2023)

PetraKIP: Personal transparency AI-based portfolio for teacher education - Zhang, Chengming; Solopova, Veronika; Romeike, Ralf; Gläser-Zikuda, Michaela; Benzmüller, Christoph; Landgraf, Tim; Hofmann, Florian; Schießl, Jessica; Plößl, Lea; Witte, Sascha (Annual Conference of the European Teacher Education Network (ETEN) “Teacher Education – Connecting Glocal”, 2023)

Portfolio-based reflective writings in teacher education - Results of a mixed methods study - Zhang, Chengming; Gläser-Zikuda, Michaela; Hofmann, Florian; Schießl, Jessica; Plößl, Lea (Gesellschaft für Empirische Bildungsforschung (GEBF) 2023, 2023)

Portfolio-based reflective writings in teacher education - results of a mixed methods study - Zhang, Chengming; Plößl, Lea; Schießl, Jessica; Hofmann, Florian; Gläser-Zikuda, Michaela (Annual Conference of the European Teacher Education Network (ETEN) “Teacher Education – Connecting Glocal”, 2023)

Pre-service teachers’ acceptance of Artificial Intelligence - Zhang, Chengming; Schießl, Jessica; Hofmann, Florian; Plößl, Lea; Gläser-Zikuda, Michaela (Digital Transformation in Teaching and Teacher Education, EARLI SIG 11 conference 2022, 2022)
Artificial intelligence (AI) applications are increasingly appearing in higher education, such as learning management systems, grading/assessment, and student information system (EDUCAUSE, 2021). Acceptance of AI has been investigated in marketing, but although AI is applied in education, there is research needed (Chikobava & Romeike, 2021) how it is accepted and which individual factors determine the use of AI. Therefore, our study (funded by the BMBF – Federal Ministry of Education and Research) aimed at the analysis of pre-service teachers’ acceptance of AI in testing their behavioral intentions regarding prospective AI technology use. Furthermore, gender effects, and differences between primary and secondary pre-service teachers’ acceptance of AI were tested. The study based on the Technology Acceptance Model (Venkatesh & Bala, 2008). An adapted German version of the instrument (Stephan, Markus & Gläser-Zikuda, 2019) was applied and consists of eight subscales with 3-4 items each and good reliabilities (α = .69 to .88): Perceived Usefulness; Perceived Ease of Use; AI Self-Efficacy; AI Anxiety; Perceived Enjoyment; Subjective Norm; Job Relevance, Behavioral Intention. Over 600 pre-service teachers participated voluntary in an online-survey administered with Unipark during a lecture in December 2021 at one German university. Analyzes included a total of 405 (294 females, 108 males, 3 third gender; mean age 21.14 years; SD = 3.82) valid responses. 75.06% of respondents were enrolled in the first semester (M = 1.76, SD = 1.48) of teacher training programs (n = 239 primary school; n = 166 secondary school). Structural equation modeling (SEM) was performed using R software. The results show that the participants report moderate levels in all subscales of AI acceptance, except for Behavioral Intention (M = 2.81, SD = .88). The proposed model achieved a good model fit (X2/df=2.28, CFI=.932, TLI=.920, RMSEA=.056[.051, .056], SRMR=.071). In the overall sample model, only AI Anxiety does not influence pre-service teachers’ intentions to use AI indirectly; the remaining study variables showed all differential effects on the intention to use AI. In the calculated female model, Behavioral Intention is significantly determined by Subjective Norm (.297***); in the male model is no significant effect. Secondary school pre-service teachers Subjective Norm has an influence on Perceived Usefulness (.320***); but this is not the case for primary school pre-service teachers. Main results of the study are presented and discussed in terms of acceptance research on AI and with respect to their relevance for teacher education and school education. <br
Keywords: Artificial intelligence; Educational technology; Pre-service teacher education; Quantitative methods

Quality of pre-service teachers’ reflections – a mixed-method study - Zhang, Chengming; Schießl, Jessica; Plößl, Lea; Hofmann, Florian; Gläser-Zikuda, Michaela (Reflexion in der Lehrkräftebildung: empirisch - phasenübergreifend - interdisziplinär, 2022)
In teacher education, reflection is seen as a crucial aspect of professionalization (Hatton & Smith, 1995) and may be supported by using a portfolio (Gläser-Zikuda, Feder, & Hofmann, 2020). To analyze reflective writing and the quality of reflection ,in recent years, researchers on the one hand use qualitative analyses (Fütterer, 2019), on the other hand automated and multi-dimensional approaches became relevant (Kovanović et al., 2018;Ullmann, 2019). Therefore, the aim of the study (funded by the BMBF) is (a) to analyze the quality of reflection using different research methods, (b) to explore the relation between linguistic features of reflective writing and the quality of reflection, and (c) to identify gender differences in linguistic features and quality of reflection. N = 709 pre-service students wrote reflections using an e-portfolio related to specific educational tasks. For the analysis, we applied a structured qualitative content analysis (Gläser-Zikuda,Hagenauer, & Stephan,2020) and computerlinguistic analyses (Pennebaker et al.,2015) to analyze the quality of reflection (Fleck & Fitzpatrick,2010) in n = 100 texts randomly selected out of 506 texts. The results of the content analysis indicate that the hierarchy of pre-service teachers’ reflections was distributed at the low and middle levels (Description = 20, Reflective Description = 61, and Dialogic Reflection = 17), while reflections at the high level (Transformative Reflection and Critical Reflection) were not. Second, the linguistic features collected using LIWC were not statistically different in morphology, syntax, and semantics at different levels of reflection. Nevertheless, the linguistic features at the morphology level were significant in the reflective writing by gender.<br
Keywords: Teacher Education; Quality of Reflection; E-Portfolio; Mixed-Methods; Linguistic Features

Reflective Writing in Higher Educaiton: A Systematic Literature Review of Domains, Aims, and Assessments - Zhang, Chengming; Hofmann, Florian; Artmann, Michaela; Plößl, Lea; Gläser-Zikuda, Michaela (Tag der Forschung 2023, 2023)

Reflexives Schreiben in der Hochschulbildung - ein systematisches Review zu Konzepten, Forschungsthemen und -methoden - Hofmann, Florian; Zhang, Chengming; Artmann, Michaela; Gläser-Zikuda, Michaela (GEBF24, 2024)

Reflexives Schreiben und Reflexionsqualität von Lehramtsstudierenden - eine Mixed-Methods-Studie - Zhang, Chengming; Schießl, Jessica; Plößl, Lea; Hofmann, Florian; Gläser-Zikuda, Michaela (Sektionstagung der Arbeitsgruppe empirische pädagogische Forschung (AEPF) in der Deutschen Gesellschaft für Erziehungswissenschaft (DGfE), 2022)
In der Lehrerbildung wird Reflexion als ein entscheidender Aspekt der Professionalisierung angesehen (Hatton & Smith, 1995), der durch die Verwendung eines Portfolios unterstützt werden kann (Gläser-Zikuda, Feder, & Hofmann, 2020). Ziel der Studie ist es daher, die Reflexionsqualität mit unterschiedlichen Forschungsmethoden zu analysieren. 709 Lehramtsstudierende verfassten Reflexionstexte im Rahmen eines E-Portfolios. Um die Reflexionsqualität (Fleck & Fitzpatrick, 2010) zu analysieren, wurde zum einen eine strukturierte qualitative Inhaltsanalyse (Gläser-Zikuda, Hagenauer, & Stephan, 2020) sowie eine computerlinguistische Analyse (LIWC; Pennebaker et al., 2015) durchgeführt. Die Ergebnisse zeigen, dass die Reflexionen der angehenden Lehrkräfte auf einem niedrigen und mittleren Niveau verteilt waren. Zweitens unterschieden sich die mit LIWC erhobenen sprachlichen Merkmale in Morphologie, Syntax und Semantik auf den verschiedenen Reflexionsebenen statistisch nicht. Allerdings waren die sprachlichen Merkmale auf der Ebene der Morphologie im reflektierenden Schreiben signifikant unterschiedlich in Bezug auf das Geschlecht. Limitationen und Implikationen der Studie werden diskutiert.<br
Keywords: Lehrer*innenbildung; Reflexionsqualität; E-Portfolio; Mixed-Methods; sprachliche Merkmale

Reflexives Schreiben von Lehramtsstudierenden: Eine Analyse mit Verfahren maschinellen Lernens und vortrainierten Sprachmodellen - Zhang, Chengming; Solopova, Veronika; Hofmann, Florian; Gläser-Zikuda, Michaela (GEBF2024, 2024)

Self-Regulated Learning of Pre-Service Teachers in Digital Learning Environments. - Zhang, Chengming; Hofmann, Florian; Kamran, Farrukh; Gläser-Zikuda, Michaela (Self-Regulated Learning – Insights and Innovations, 2024)
Nowadays, “being digital" has been largely realized, especially in education. The continuous renewal of Information Communication Technology (ICT) and Artificial Intelligence (AI) leads to an educational revolution. Therefore, teaching and learning activities are no longer restricted by time, space, CE and humans. As a result, future qualified teachers with high self-regulation competence and strong digital competence are in great need. However, research studies reveal that many teachers directly replicate traditional classrooms and fail to conduct virtual activities with possible digital resources and the application of AI. Confronted with such challenges, future teachers do often not accept AI and feel unprepared for future teaching. This chapter provides an overview of research theories, assessment techniques, and support instruments in related research area
Keywords: self-regulated learning; digital competence; AI acceptance; teacher education research

The Formative Assessment and Feedback Practices at Language and Science Classrooms at Public Higher Education Institutions in Pakistan - Kamran, Farrukh; Zhang, Chengming; Gläser-Zikuda, Michaela (Annual Conference of the European Teacher Education Network (ETEN) “Teacher Education – Connecting Glocal”, 2023)