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

doi:10.3390/educsci13121213

Cited by 5 publications

(2 citation statements)

References 65 publications

(91 reference statements)

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“…Our finding is contrary to pertinent literature stating that students mainly reflect on a descriptive level [ 15 , 35 , 61 , 62 ] but is consistent with previous studies indicating that the highest level of reflection, critical reflection , is unlikely to occur frequently [ 57 ]. That students commonly do not reach the level of critical reflection was described in previous studies that analysed reflection exercises in health professions [ 63 – 65 ] as well as in other disciplines [ 61 , 66 – 68 ].…”

Section: Discussionmentioning

confidence: 91%

“…However, there is broad evidence that ongoing feedback can improve reflection [ 70 , 73 , 74 ], and we plan to incorporate it into our subsequent courses. Since feedback given by lecturers requires resources, additional future possibilities could be the complementary use of automated feedback and more advanced artificial intelligence technologies to analyse and assess reflective writing, as well as to provide personalised feedback to students [ 68 ].…”

Section: Discussionmentioning

confidence: 99%

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Reflect to interact – fostering medical students’ communication through reflection-focused e-learning

Janssen,

Schick,

Neurohr

et al. 2024

BMC Med Educ

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Background Previous research indicates that reflection can foster medical communication competence. However, best practices for embedding reflection in online medical education are missing. This study examined how reflection processes can be promoted and embedded in an e-learning course on physician–patient communication to foster learning. Methods We investigated three differently designed e-learning conditions featuring different proportions of reflection triggers and compared their effects on students’ reflections. We had 114 medical students in their first clinical year complete one of the variants: video modelling (VM, n = 39), video reflection (VR, n = 39), or a variant merging both approaches (VMR, n = 36). Each student wrote a total of nine reflections based on the same guiding questions at three time points embedded in the course. The students’ levels of reflection were measured using an adapted version of the REFLECT rubric (scale 0–18). Results Students of all course variants achieved good levels of reflection beyond the descriptive level at all three time points, with no significant differences between the variants. The mean reflection scores at the end of the course were M = 14.22 for VM (SD = 2.23), M = 13.56 for VR (SD = 2.48), and M = 13.24 for VMR (SD = 2.21). Students who completed VM showed significantly improved levels of reflection over the course, whereas we found no statistically significant development for those in VR or VMR. The reflection scores correlated significantly positively with each other, as did the text lengths of the written reflections. Reflection scores also correlated significantly positively with text length. Conclusions Our study offers a teaching strategy that can foster good levels of reflection, as demonstrated in the three e-learning variants. The developed reflection prompts can be easily embedded in various e-learning environments and enable reflections of good quality, even in settings with limited available teaching time.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Reflect to interact – fostering medical students’ communication through reflection-focused e-learning

Janssen,

Schick,

Neurohr

et al. 2024

BMC Med Educ

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Classification of reflective writing: A comparative analysis with shallow machine learning and pre-trained language models

Zhang,

Hofmann,

Plößl

et al. 2024

Educ Inf Technol

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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.

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Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysis

Zhang,

Hu,

et al. 2024

Educ Inf Technol

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Evaluating Reflective Writing in Pre-Service Teachers: The Potential of a Mixed-Methods Approach

Cited by 5 publications

References 65 publications

Reflect to interact – fostering medical students’ communication through reflection-focused e-learning

Reflect to interact – fostering medical students’ communication through reflection-focused e-learning

Classification of reflective writing: A comparative analysis with shallow machine learning and pre-trained language models

Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysis

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