2017
DOI: 10.1111/bjet.12597
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Facial micro‐expression states as an indicator for conceptual change in students' understanding of air pressure and boiling points

Abstract: Utilizing facial recognition technology, the current study has attempted to predict the likelihood of student conceptual change with decision tree models based on the facial micro-expression states (FMES) students exhibited when they experience conceptual conflict. While conceptual change through conceptual conflicts in science education is a well-studied field, there is little research done on conceptual change through conceptual conflict in terms of students' facial expressions. As facial expressions are one… Show more

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Cited by 17 publications
(14 citation statements)
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“…Especially in scientific experiments, we find that when the experimental results are revealed, the dominant expression of students is first surprised, then negative, and the probability of knowledge change is higher [ 47 ]. Surprise, sadness, and disgust are also key facial expressions used to predict the change in students’ knowledge based on conflicting scenarios [ 48 ].…”
Section: Introductionmentioning
confidence: 99%
“…Especially in scientific experiments, we find that when the experimental results are revealed, the dominant expression of students is first surprised, then negative, and the probability of knowledge change is higher [ 47 ]. Surprise, sadness, and disgust are also key facial expressions used to predict the change in students’ knowledge based on conflicting scenarios [ 48 ].…”
Section: Introductionmentioning
confidence: 99%
“…This framework fits the current era as educational research is flooded with new (Horizontal) means of measuring students' cognitive processing (e.g., eye tracking; Chaulic et al 2020) and meta-cognitive processing (e.g., trace data; Rogiers, et al, 2020). This Horizontal drive for innovation of measurement continues to push into complex areas such as emotion (facial recognition ;Chiu, et al, 2019;Dingle, et al, 2016;skin conductance, Järvenoja, et al 2018;Lehikoinen et al, 2019) and motivation (neuroscience; Hidi, 2016;Mayer, 2017). The considerable momentum behind this drive for alternatives to self-report measurement arise in large part with a longstanding dissatisfaction with their intra-psychic nature and the general lack of Lateral development in these measures.…”
Section: Lateral and Vertical Innovation: Both Are Criticalmentioning
confidence: 93%
“…In particular, in a counterintuitive scientific experiment demonstration, it was found that if the students' dominant expression was surprised followed by negative expressions when the result of the experiment was revealed, there will be a higher chance of conceptual change (Chiu, Yu, Liaw, & Lin, 2015). Surprised, sad, and disgusted were also identified as key facial expressions in the prediction of student conceptual change in a conceptual conflict-based scenario (Chiu et al, 2019). Based on the findings of the above literature, the current study has taken one step further by examining the meaning of different types of facial microexpression states (FMES), i.e., facial expressions that often last only a few micro-seconds and difficult for the naked eyes to capture, in terms of prior knowledge and multiple representation.…”
Section: Emotions Learning and Facial Expressionsmentioning
confidence: 99%
“…FaceReader's analysis is based on Ekman's seven universal facial expression states (Ekman, 1970), and the facial expressions were categorized into positive (happy), negative (sad, angry, scared, and disgusted), surprised, and neutral. It is a commercially available software that has been used in various disciplines and research (e.g., Chiu et al, 2014;Chiu et al, 2019;Lewinski, den Uyl, & Butler, 2014;Liaw et al, 2014;Maison & Pawłowska, 2017;Skiendziel, Rösch, & Schultheiss, 2019;Xie, Yu, Niu, & Li, 2019). The three key segments of students' facial expression videos mentioned above (i.e., E, T, and A) were the explanatory variables.…”
Section: Instrument and Proceduresmentioning
confidence: 99%
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