Now showing 1 - 10 of 34
  • Publication
    Open Access
    Investigating secondary school students' academic emotions in data science learning
    (Asia-Pacific Society for Computers in Education, 2024) ; ; ;
    Ker, Chin-Lee
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    Ong, Aloysius Kian-Keong
    ;
    Cultivating students' data science knowledge and skills is pressing and challenging, given its interdisciplinary nature, students' limited prior knowledge, and teachers' insufficient training. In data science learning, students may experience various academic emotions. Understanding what emotions students experience, how these emotions are associated with their perceived learning, and under what conditions they experience intensive emotions is critical to informing the design of data science programs and better supporting students. This study collected 839 emotion survey responses from 67 secondary school students in two cycles of a two-day out-of-school data science program. The program engaged students in collaborative inquiries on authentic problems through data science practices with the support of teachers, researchers and facilitators. We found that frustration, interest, surprise and happiness positively predicted students' perceived learning, whereas anxiety negatively predicted perceived learning. Students experienced peaks of positive emotions after an expert's enthusiastic introduction talk to data science in the first cycle and after one-to-one face-to-face consultations with data science experts in the second cycle. However, sharing their progress and challenges with the data science expert in the first cycle and preparing for presentations in both cycles made them experience intense negative emotions such as anxiety, frustration, and confusion. These findings provide implications for designing data science programs to elicit students' positive learning experiences and reduce intensive negative emotions.
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  • Publication
    Metadata only
    Data, datafication and data citizenship: Managing, moderating and ameliorating testing in Singapore
    (Elsevier, 2025)
    Hardy, Ian
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    ;
    Hou, Chenyu
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    M. Obaidul Hamid
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    Reyes, Vicente
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    Phillips, Louise G.
    In this article, we draw upon notions of datafication and data citizenship to explore assessment practices in Singapore schools. Interviews with teachers, principals and students illustrated how they were actively involved in a much more ‘educative’ form of engagement with data, characterized by efforts to ameliorate consequential negative effects of testing, even as meritocratic tendencies challenged such efforts. The research highlights the benefits of teacher, principal and student active engagement with data, reflecting aspects of data citizenship; this ensures that more limited and limiting concerns about increased focus upon data, especially in quantitative forms, are not left unchecked.
      7
  • Publication
    Metadata only
    Examining university instructors’ conceptions and perceived changes in knowledge building professional development
    (International Society of the Learning Sciences, Inc., 2023)
    Lin, Feng
    ;
    Low, Wei Yan
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    ;
    This is a work-in-process research project aiming at examining the design of Knowledge Building professional development (KBPD) to foster university instructors’ conceptions of teaching and learning and teaching practices. 10 instructors from the same university joined this study. Multiple sources of data were collected, including surveys, classroom and online artefacts, and interviews. Analysis of pre- and post-surveys showed that the participants hold more constructivist conceptions about teaching and learning after attending KBPD. The classroom reflection artefacts showed that they were more inclined to apply the KB principles in their own classes, and that they regarded the epistemological role of their students have shifted more towards knowledge constructors/creators in their classrooms after attending the KBPD. Interview analysis further showed in what ways they have changed their conceptions and perceived practices. Implications for future design of KBPD were discussed.
      30
  • Publication
    Open Access
    Share and embrace demographic and location diversity: Creating an Instagram-based inclusive online learning community
    (Wiley, 2022)
    Zhu, Wangda
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    Hua, Ying
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    ;
    Wang, Luping
    It is critical to create an inclusive online learning environment for students with diverse demographic information studying in different environments, especially during the COVID-19 pandemic when they are disconnected from peers. Guided to create an inclusive online learning community by situated learning theory and community of practice, both of which advocate learning in context and community, we invited 115 undergraduate students to post photos related to environmental psychology concepts and their surrounding environments and discussed their postings on Instagram over eight weeks. To understand the inclusiveness of the community and students' perception, we collected their posts by searching designated hashtags and interviewed representatives of participants using a stratified sampling strategy. Through network analysis of 272 posts and qualitative analysis of 22 in-depth interviews, we found that when participants shared and discussed their surroundings and environmental psychology concepts on Instagram, their learning community was inclusive regarding gender, ethnicity, and program. Student participants' centrality and influence were more relevant to whether and how they expressed their identities in the community through posts. We further discuss how our findings could inform to create inclusive and active communities in the future.
    WOS© Citations 4Scopus© Citations 7  71  194
  • Publication
    Open Access
    Prompt-based and fine-tuned GPT models for context-dependent and -independent deductive coding in social annotation
    (Association for Computing Machinery, 2024)
    Hou, Chenyu
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    ;
    Zheng, Juan
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    Zhang, Lishan
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    Huang, Xiaoshan
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    Zhong, Tianlong
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    Li, Shan
    ;
    Du, Hanxiang
    ;
    Ker, Chin Lee
    GPT has demonstrated impressive capabilities in executing various natural language processing (NLP) and reasoning tasks, showcasing its potential for deductive coding in social annotations. This research explored the effectiveness of prompt engineering and fine-tuning approaches of GPT for deductive coding of context dependent and context-independent dimensions. Coding context dependent dimensions (i.e., Theorizing, Integration, Reflection) requires a contextualized understanding that connects the target comment with reading materials and previous comments, whereas coding context-independent dimensions (i.e., Appraisal, Questioning, Social, Curiosity, Surprise) relies more on the comment itself. Utilizing strategies such as prompt decomposition, multi-prompt learning, and a codebook-centered approach, we found that prompt engineering can achieve fair to substantial agreement with expert labeled data across various coding dimensions. These results affirm GPT’s potential for effective application in real-world coding tasks. Compared to context-independent coding, context-dependent dimensions had lower agreement with expert-labeled data. To enhance accuracy, GPT models were fine-tuned using 102 pieces of expert-labeled data, with an additional 102 cases used for validation. The fine-tuned models demonstrated substantial agreement with ground truth in context-independent dimensions and elevated the inter-rater reliability of context-dependent categories to moderate levels. This approach represents a promising path for significantly reducing human labor and time, especially with large unstructured datasets, without sacrificing the accuracy and reliability of deductive coding tasks in social annotation. The study marks a step toward optimizing and streamlining coding processes in social annotation. Our findings suggest the promise of using GPT to analyze qualitative data and provide detailed, immediate feedback for students to elicit deepening inquiries.
    Scopus© Citations 1  12  146
  • Publication
    Metadata only
    Enhancing student self-assessment using technology
    (Routledge, 2022) ;

    This chapter will explore how the affordances of technological platforms (i.e. web versions and mobile apps) can enhance student self-assessment (SSA) and as a result, generating feedback, promoting learning, and improving performance (Andrade, 2019). It begins with recapping the key features of SSA as presented in Chapter 2 and the conditions conducive for its success in Chapter 3. This is followed by a review of literature on the advantages that technology may offer in the whole SSA process. To illustrate such advantages, three cases on how teachers use technology-enhanced SSA are presented. The chapter concludes with a reflection on the limitations and future directions in this area.

      6
  • Publication
    Metadata only
    Trajectories and predictors of adolescent purpose development in self‐driven learning
    (Wiley, 2025)
    Ratner, Kaylin
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    Xie, Hou
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    Estevez, Melody
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    Burrow, Anthony L.
    Purpose offers several important benefits to youth. Thus, it is necessary to understand how a sense of purpose develops in supportive contexts and what psychological resources can help. From 2021 to 2022, this study investigated purpose change among 321 youth (Mage = 16.4 years; 71% female; 25.9% Black, 33.3% Asian, 15.6% Hispanic/Latinx, 13.4% White, 9.7% multiracial) participating in GripTape, a ~10-week self-driven learning program. Many youth started with high initial purpose that increased throughout enrollment (Strengthening), whereas others began with slightly lower purpose that remained stable (Maintaining). For each unit increase in baseline agency, youth were 1.6x more likely to be classified as Strengthening. As such, agency may be a resource that helps youth capitalize on certain types of environments.
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  • Publication
    Metadata only
    Beyond the cognitive dimension: Emotion patterns in productive and improvable knowledge building discourse
    (2023)
    Hou, Chenyu
    ;
    ;
    Yang Yuqin

    Knowledge Building is a pedagogical approach emphasizing students' collective responsibility to continuously improve their community knowledge. Various emotions may arise during Knowledge Building activities because of students’ diverse ideas, theory-building and cognitive disequilibrium and equilibrium. These emotions may differ in inquiry threads at different discourse development levels. An inquiry thread is a sequence of notes addressing the same problem or topic. This study examines the frequency and sequential patterns between undergraduate students’ productive and improvable Knowledge Building inquiry threads recorded in Knowledge Forum. We found that emotions reflected in inquiry threads tend to be self-repeated. A series of positive and negative activating emotions were reflected in productive collaborative inquiry threads, suggesting students engaged in the discussion despite conflicting ideas and various emotions. On the other hand, improvable collaborative inquiry threads displayed activating to deactivating emotion transitions, such as joy to boredom, which shows that students might disengage from the discussion.

      37
  • Publication
    Metadata only
    Enhancing undergraduates’ engagement in a learning community by including their voices in the technological and instructional design
    (Elsevier, 2024)
    Zhu, Wangda
    ;
    ;
    Hua, Ying

    Over the past decades, Social Networking Tools (SNT) have been applied in educational settings to support students' engagement in learning communities. Previous studies suggested the positive effects of including students' voices in technological and instructional design. However, educators usually cannot revise the features of SNT as they like, which may limit the possibility of enhancing students' engagement (i.e., cognitive, emotional, and social-behavioral engagement). Therefore, this study explored whether changing SNT technological and instructional design based on students' voices can improve engagement. We developed a photo-sharing web-based SNT and examined whether refining the SNT and instruction design based on students' input would enhance their multifaceted engagement in a learning community. We collected the opinions and feedback from 114 undergraduate students in an environmental psychology course at a private university in the USA using surveys every three weeks. We refined the technological and instructional design accordingly. Students' engagement was measured four times during the semester, and after the semester, nine students were interviewed with regard to how the technological and instructional design changes influenced their engagement. With successive iterations, we found that students' cognitive and emotional engagement significantly improved, while their social-behavioral engagement did not change significantly during the study. Interview results further explained how the design changes influenced students' engagement. The findings suggested soliciting students’ input into SNT technological and instructional design can benefit their engagement in a learning community, while engagement was also influenced by many other factors.

    Scopus© Citations 1  54
  • Publication
    Metadata only
    Investigating the effect of emotional tone on learners’ reading engagement and peer acknowledgement in social annotation
    (Australasian Society for Computers in Learning in Tertiary Education, 2024)
    Huang, Xiaoshan
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    Zheng, Juan
    ;
    Li, Shan
    ;
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    Du, Hanxiang
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    Zhong, Tianlong
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    Hou, Chenyu
    ;
    Lajoie, Susanne
    Social annotation fosters collaborative learning by encouraging knowledge sharing and a community of inquiry. However, research has primarily focused on the cognitive aspect of social annotation. This study aims to contribute an emotional perspective to the existing literature on social annotation. Specifically, we used the valence-aware dictionary for sentiment reasoning algorithm to measure students’ emotional tones in 1,954 comments posted during social annotation. We then utilised linear mixed-effect models to examine the effect of emotional tone on students’ reading engagement and peer acknowledgement, respectively. Our findings indicate that students who posted more positive sentiment comments were more likely to spend more time engaging in social annotation and receive peer acknowledgement. These findings offer insights into the significance of emotional tone in social annotation and the design of scaffolding strategies to foster positive emotional tone.
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