Personalized refutation texts best stimulate teachers' conceptual change about multimedia learning.
Background: Previous research has shown that teachers hold misconceptions about multimedia learning (e.g., multimedia instruction needs to be adapted to students' learning styles), which may be at odds with evidence‐based teaching. Objectives: Refutation texts are a classical method to reduce miscon...
| Publicado en: | Journal of Computer Assisted Learning Vol. 38; no. 4; pp. 977 - 993 |
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| Autores principales: | , , |
| Formato: | algorithm questionnaire/scale research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157845812&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157845812 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2022 vid: 38 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157845812 156211538 157845812 157845812 10.1111/jcal.12671 157845812 ppf: 977 ppct: 16 formats: tig: atl: Personalized refutation texts best stimulate teachers' conceptual change about multimedia learning. aug: au: Dersch, Anna‐Sophia Renkl, Alexander Eitel, Alexander affil: Department of Educational Psychology, University of Giessen, Giessen, Germany sug: subj: Teachers Education Professional Knowledge Multimedia Education Algorithms Human Computer-Assisted Instruction Funding Source Internet Questionnaires Guilt Shame Online Education Exploratory Research Male Female Adult Middle Age Pretest-Posttest Design Chi Square Test Germany Mediation Analysis Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background: Previous research has shown that teachers hold misconceptions about multimedia learning (e.g., multimedia instruction needs to be adapted to students' learning styles), which may be at odds with evidence‐based teaching. Objectives: Refutation texts are a classical method to reduce misconceptions and thus to stimulate conceptual change. We wanted to know whether making use of a computer algorithm to personalize refutation texts would best initiate teachers' conceptual change. Methods: We designed an online experiment, in which N = 129 in‐service teachers read either (1) expository texts (without direct refutation), (2) common refutation texts, or (3) personalized refutation texts. The teachers filled in a misconception questionnaire pre and post to assess their conceptual change. Results and Conclusions: Statistical analyses revealed that personalized refutation texts initiated the strongest conceptual change, which was driven by increased feelings of guilt and shame. Common refutation texts did not foster teachers' conceptual change as compared to expository texts. These findings indicate that refutation texts should be personalized for experienced practitioners such as teachers. Takeaways: Personalized refutation seems to be promising in the context of online teacher training programs. Further research should test to which extent the present findings also apply to other groups of experienced learners or practitioners. Lay Description: What is already known about this topic?: Teachers hold misconceptions about multimedia learning (e.g., learning materials should be adapted to students' individual learning styles, such as visualizers or verbalizers).Refutation texts, naming a commonly held misconception, disproving it and introducing a scientific explanation, are a common means to reduce misconceptions.Personalization fosters learning by drawing the learner's attention toward the discrepancy between their own beliefs and the learning material, further creating an impasse experience.Said impasse experience may trigger teachers' conceptual change, as, for teachers' conceptual change, a certain degree of discomfort is required. Yet, anger, caused by lessoning teachers on their topic may cause repulse and hamper learning. What this paper adds?: With a computer algorithm, we can efficiently personalize refutation texts by automatically matching them to teachers' answers in a pre‐test. Such a personalized refutation instruction may especially foster conceptual change.Within a randomized experiment, the personalized refutation instruction worked best compared to common refutation texts and expository texts.Feelings of guilt and shame moderated the effect of a personalized refutation, as teachers felt more addressed in their misconceptions and thus experienced the required impasse experience.Feelings of anger did not play an important role within our experiment. The implications of study findings for practitioners: Computer algorithms enable efficient personalization of instruction to better deal with heterogeneous groups of learners (e.g., with big differences in prior knowledge or experience, such as in the case of in‐service teachers).Refutation texts work better for teachers when they are personalized. Common refutation texts do not work better than expository texts.An advantage of digital instruction is the use of algorithms to efficiently personalize instructions even for larger groups. pubtype: Academic Journal doctype: algorithm questionnaire/scale research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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