Enabling feedback seeking, agency and uptake through dialogic screencast feedback

Wood, James M.

doi:10.1080/02602938.2022.2089973

Cited by 12 publications

(4 citation statements)

References 68 publications

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“…In this approach, educators use various technologies to provide comment on students' work (Munshi & Deneen, 2018). Such technologies can be categorised broadly into: text (Cheng & Zhang, 2021;Elola & Oskoz, 2016); audio (Cavaleri et al, 2019;Hennessy & Forrester, 2014;Mahoney et al, 2019); video (Cavaleri et al, 2019;Crook et al, 2012;Yiğit & Seferoğlu, 2023); screencast (Anson et al, 2016;Wood, 2023); and AI-feedback (Hooda et al, 2022;Rad et al, 2023)-all of which resonate with learning styles in the VARK model, namely visual, auditory, read/write, and kinaesthetic (Fleming & Mills, 1992).…”

Section: Modes Of Communicating Feedback and Processesmentioning

confidence: 99%

“…The consensus in the literature suggested that technology-mediated feedback enhanced the feedback process by allowing greater detail and more constructive advice, making learners feel that the feedback was more personalised (Anson et al, 2016;Orlando, 2016;Wood, 2023). As such, it motivated students to respond by actively engaging with and enacting the feedback.…”

Section: Modes Of Communicating Feedback and Processesmentioning

confidence: 99%

“…As such, it motivated students to respond by actively engaging with and enacting the feedback. Despite these benefits, educators needed to be mindful of the potential technical barriers, such as the need for access to appropriate technology and proficiency in using feedback software and tools, as well as the risk of potential misinterpretation of feedback, especially for NESB students (Wood, 2023).…”

Section: Modes Of Communicating Feedback and Processesmentioning

confidence: 99%

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Artificial Intelligence Use in Feedback: A Qualitative Analysis

Pang,

Kootsookos,

Cheng

2024

JUTLP

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Feedback, particularly the formative or ‘feed-forward’ type is important for students in higher education to understand their errors and improve their expression and clarity of ideas. While technology-assisted feedback modes, e.g., audio or video are prevalent, ensuring their efficacy and succinctness, particularly for non-English-speaking background (NESB) educators can be challenging. This study investigates the attitudes and experiences of NESB educators in the School of Engineering of RMIT University, with a focus on their use of AI-assisted tools for providing feedback to students in higher education settings. Utilising a survey, the researchers examined how personal and linguistic attributes influenced feedback strategies and explored the educators' perspectives on integrating AI tools, such as ChatGPT and BARD, in their teaching practice and to enhance student engagement with the feedback they received. Through thematic analysis the findings reveal that personal background and linguistic proficiency significantly influenced the provision of feedback. Furthermore, even though educators had different levels of familiarity with AI-assisted tools, there was a general consensus on the potential utility of these tools for improving feedback provision. These will require targeted staff training, careful human oversight to ensure quality and avoid bias, and customised AI training to align feedback with individual teaching styles.

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Section: Modes Of Communicating Feedback and Processesmentioning

confidence: 99%

Section: Modes Of Communicating Feedback and Processesmentioning

confidence: 99%

Section: Modes Of Communicating Feedback and Processesmentioning

confidence: 99%

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Artificial Intelligence Use in Feedback: A Qualitative Analysis

Pang,

Kootsookos,

Cheng

2024

JUTLP

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“…Lastly, extended technology-mediated dialogues supported co-regulation and a shared meaning making process developing the quality of actionable feedback generated. These themes were further validated by Wood's (2022b) adoption of a qualitative case-based approach, exploring the use of dialogic teacher screencast feedback using written data, reflections, and surveys with 13 undergraduate students in South Korea. This paper further demonstrated that students perceived screencast feedback to be easier to use, that where 'transmission' of feedback failed, technology-mediated dialogues helped them understand and apply feedback, and learners found video feedback to motivate feedback use.…”

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confidence: 99%

Symposium Report: Malaysian Association of Applied Linguistics (Maal) Symposium on “How to Make Peer Feedback Work”

Xie

Nimehchisalem

2022

JLC

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The authors co-chaired this symposium, entitled How to Make Peer Feedback Work and hosted online by the Malaysian Association of Applied Linguistics (MAAL) on 1 September 2022. The speakers, Juuso Henrik Nieminen (Assistant Professor, University of Hong Kong), James Wood (Associate Teaching Professor, Seoul National University), Wei Wei (Associate Professor, Macao Polytechnic University), Tingting Liu (PhD candidate, Nanyang Technological University), and Vahid Aryadoust (Associate Professor, Nanyang Technological University) delivered enlightening presentations to over 50 participants, in turn, discussing topics related to authentic assessment, dialogic peer screencast feedback, self-efficacy, teacher video feedback, and gender and academic major bias in relation to peer assessment.

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Epistemic agency: a link between assessment, knowledge and society

Nieminen,

Ketonen

2023

High Educ

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In this conceptual article, we discuss the idea of students’ epistemic agency as an overlooked link between assessment, knowledge and society. We transcend the contemporary discourses around assessment that focus on its authenticity and student-centredness and instead investigate assessment from the viewpoints of knowledge and knowing. This approach sees assessment as functioning not only as a promoter of student learning but also as a means to prepare students to be responsible graduates and citizens as epistemic agents. First, we adapt the theory of epistemic agency—that is, students’ capability to agentically evaluate, produce, use and transform knowledge—by situating it within the specific context of assessment. Second, we suggest practice-oriented ideas for assessment and feedback design to nurture epistemic agency. Overall, we do not depict epistemic agency as yet another ‘soft skill’ in higher education but as a necessary focal point for assessment that aims to nurture a transformative relationship between students and knowledge. We suggest epistemic agency as a powerful concept in understanding and nurturing the three-way engagement between assessment, knowledge and society. This concept allows us to understand whether and how assessment shapes students as epistemic agents.

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Enabling feedback seeking, agency and uptake through dialogic screencast feedback

Cited by 12 publications

References 68 publications

Artificial Intelligence Use in Feedback: A Qualitative Analysis

Artificial Intelligence Use in Feedback: A Qualitative Analysis

Symposium Report: Malaysian Association of Applied Linguistics (Maal) Symposium on “How to Make Peer Feedback Work”

Epistemic agency: a link between assessment, knowledge and society

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