Designing learner-centred text-based feedback: a rapid review and qualitative synthesis

Ryan, Tracii; Henderson, Michael; Ryan, Kris; Kennedy, Gregor

doi:10.1080/02602938.2020.1828819

Cited by 18 publications

(27 citation statements)

References 58 publications

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“…Then, they [30] used LA approaches to analyse the correlation between the textual features and student grade changes. Though Nicoll et al [30] demonstrated a method to understand the effect of feedback content on student learning outcomes, we argued that there was no explicit discussion between the textual features in their work [30] and the theoretically grounded attributes in the learner-centred feedback framework [35]. Additionally, the feedback effect (i.e., improvement in students' learning outcomes on the subsequent task) was not substantial since the connection between the two assignments was unclear in their work.…”

Section: Introductionmentioning

confidence: 82%

“…Students indicated that the quality of feedback content (e.g., actionable information for improvement on subsequent tasks) was the most effective aspect of feedback, whereas instructors indicated that feedback should be provided in the context of connected tasks (i.e., the feedback provided for the first task is connected with the subsequent task) [11]. These indications of effective feedback were further affirmed by a recent learner-centred feedback framework [35], which demonstrated a comprehensive set of effective feedback attributes (e.g., comments that provide actionable information for future performance) for textual feedback in higher education.…”

Section: Introductionmentioning

confidence: 97%

“…Effective feedback should deliver high-quality content and demonstrate the effect on student improvement such as improvement in learning outcomes and conceptual understanding [11,35]. To understand how feedback content affects student improvement, researchers employed Learning Analytics (LA) approaches to analyse the correlation between feedback content and the students' learning outcomes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learner-centred Analytics of Feedback Content in Higher Education

Lin¹,

Dai²,

Lim³

et al. 2022

Preprint

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Feedback is an effective way to assist students in achieving learning goals. The conceptualisation of feedback is gradually moving from feedback as information to feedback as a learner-centred process. To demonstrate feedback effectiveness, feedback as a learner-centred process should be designed to provide quality feedback content and promote student learning outcomes on the subsequent task. However, it remains unclear how instructors adopt the learner-centred feedback framework for feedback provision in the teaching practice. Thus, our study made use of a comprehensive learner-centred feedback framework to analyse feedback content and identify the characteristics of feedback content among student groups with different performance changes. Specifically, we collected the instructors' feedback on two consecutive assignments offered by an introductory to data science course at the postgraduate level. On the basis of the first assignment, we used the status of student grade changes (i.e., students whose performance increased and those whose performance did not increase on the second assignment) as the proxy of the student learning outcomes. Then, we engineered and extracted features from the feedback content on the first assignment using a learner-centred feedback framework and further examined the differences of these features between different groups of student learning outcomes. Lastly, we used the features to predict student learning outcomes by using widely-used machine learning models and provided the interpretation of predicted results by using the SHapley Additive exPlanations (SHAP) framework. We found that 1) most features from the feedback content presented significant differences between the groups of student learning outcomes, 2) the gradient boost tree model could effectively predict student learning outcomes, and 3) SHAP could transparently interpret the feature importance on predictions.

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Section: Introductionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learner-centred Analytics of Feedback Content in Higher Education

Lin¹,

Dai²,

Lim³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Menurut Kisnanto (2016), pemberian tanggapan atas tugas yang diberikan kepada mahasiswa dapat meningkatkan pemahaman mahasiswa terhadap materi yang diberikan. Mahasiswa yang menerima umpan balik dapat menilai pemahaman dan segera memperbaiki kesalahan yang dilakukan (Ryan et al, 2020). Sebaliknya mahasiswa akan kebingungan jika jawaban yang diberikan oleh dosen tidak jelas (Zhang & Zheng, 2018).…”

Section: Hubungan Antar Variabel Penelitianunclassified

Komunikasi Interpersonal Dan Kinerja Belajar Taruna Program Studi Penyuluhan Perikanan Politeknik Aup Selama Pandemi Covid-19

Yudistira

Muljono²,

Taryoto³

2022

JURNAL KEBIJAKAN PERIKANAN INDONESIA

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Selama pembelajaran daring akibat pandemi Covid-19 di Program Studi Penyuluhan Perikanan Politeknik AUP, tujuh taruna tidak menyelesaikan pendidikan di Semester I. Penelitian sebelumnya menunjukkan hubungan antara kinerja belajar dengan komunikasi interpersonal, tingkat kehadiran, efikasi diri terhadap pembelajaran daring, kemandirian belajar, dan motivasi akademik. Penelitian ini bertujuan untuk menguji hubungan antara efikasi diri terhadap pembelajaran daring, kemampuan komunikasi interpersonal, kemandirian belajar, motivasi akademik, dan tingkat kehadiran terhadap kinerja belajar. Pengambilan sampel dilakukan menggunakan metode sensus. Dari 311 orang taruna, hanya 200 taruna mengisi instrumen penelitian. Kuesioner yang digunakan adalah Interpersonal Communication Competence Inventory, Online Learning Readiness Scale, Online Self-regulated Learning Questionnaire, dan Academic Motivation Scale. Data indeks prestasi, indeks prestasi kumulatif, dan tingkat kehadiran diperoleh dari Bagian Administrasi Akademik dan Ketarunaan Politeknik AUP. Data dianalisis menggunakan Partial Least Square (PLS) dengan hierarchical component models dengan dua tingkatan variabel. Dimensi setiap variabel sebagai tingkat pertama (first order) dan variabel efikasi diri terhadap pembelajaran daring; kemampuan komunikasi interpersonal; kemandirian belajar; motivasi sebagai tingkat kedua (second order). Model pengukuran telah memenuhi validitas dan reliabilitas. Sedangkan model struktural mempunyai koefisien determinasi yang lemah. Model struktural yang dibuat terpisah untuk semester II, semester IV, dan Semester VI dan VIII memperlihatkan nilai koefisien determinasi yang lebih besar dibandingkan dengan gabungan keseluruhan taruna. Hasil pengujian hipotesis memperlihatkan, hanya tingkat kehadiran yang berhubungan nyata dengan kinerja belajar pada seluruh model. Kemampuan komunikasi interpersonal juga berhubungan nyata dengan kinerja belajar pada taruna semester II. The Covid-19 pandemic caused all education activities to shift to online learning. During online learning, seven cadets from the Fisheries Extension Study Program of The Jakarta Technical University of Fisheries were dropped out in the first semester. Previous research has shown a relationship between learning performance and interpersonal communication, attendance level, self-efficacy towards online learning, self-regulated learning, and academic motivation. This study examines the relationship between self-efficacy toward online education, interpersonal communication skills, self-regulated learning, academic motivation, attendance levels, and learning performance. The research sample was obtained through a census. Of 311 cadets, only 200 cadets who fill the questionnaires. The questionnaires used were Interpersonal Communication Competence Inventory, Online Learning Readiness Scale, Online Self-regulated Learning Questionnaire, and Academic Motivation Scale. GPA and attendance level were collected from the Academic Administration of JTFU. Data were analyzed using Partial Least Square (PLS) with hierarchical component models with two levels of variables. Dimensions of each variable in the first order, then self-efficacy towards online learning; interpersonal communication skills; self-regulated learning; academic motivation in the second order. Evaluation of measurement models meets the validity and reliability criteria, but the structural model has a weak coefficient of determination. The structural model made separately for Semester II, Semester IV, and Semester VI and VIII has a more significant resolution coefficient than the total of cadets. Hypothesis testing showed only attendance levels related to learning performances in all models. Interpersonal communication skills are also significantly associated with the learning performance of the second-semester cadets.

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“…In order to make feedback effective, learners' sense-making and action-taking are critical in the feedback process, which requires students to have a certain level of feedback literacy and the educator to facilitate a dialogic feedback process that encourages students to take active roles in making sense of feedback and benefiting from this process (Price et al, 2010). On the other hand, a recent study argues that learner-centred feedback can enhance the sustainability of the feedback by placing learners as a key agents in the process (Ryan et al, 2020). The authors proposed three key conditions to be met in order to make the learner-centred feedback effective: sense-making, agency and impact.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Supporting Dialogic Feedback Processes Using Learning Analytics: the Educators’ Views on Effective Feedback

Jiang

Martínez-Maldonado²,

Li³

et al. 2022

APUBS

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Feedback plays a crucial role in learning. Yet, higher education continues to face challenges regarding facilitating effective feedback processes. One of the challenges is the difficulty to track how students interact with feedback and the impact of feedback on learning outcomes. Learning analytics (LA) has opened up opportunities to enhance feedback practice with a wide array of data. However, most research seeks to deliver data-driven feedback rather than understanding how students make use of feedback and how educators can use learning analytics to support students in this process. As a first step to address this gap, our study investigated educators’ views of challenges and elements of effective feedback processes in addition to their perceptions of data-driven feedback. The study found that feedback design (e.g., feedback purpose, content, and structure), educator-related factors (e.g., time constraints and resource limitations), and student-related factors (e.g., disposition, self-regulation, and sense-making) can have positive or negative impacts on the feedback process. It also highlights the need for the development of student feedback literacy. Based on the findings, we proposed ideas for an LA-based feedback tool that can be used to facilitate a dialogic feedback process and address challenges with feedback.

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Designing learner-centred text-based feedback: a rapid review and qualitative synthesis

Cited by 18 publications

References 58 publications

Learner-centred Analytics of Feedback Content in Higher Education

Learner-centred Analytics of Feedback Content in Higher Education

Komunikasi Interpersonal Dan Kinerja Belajar Taruna Program Studi Penyuluhan Perikanan Politeknik Aup Selama Pandemi Covid-19

Towards Supporting Dialogic Feedback Processes Using Learning Analytics: the Educators’ Views on Effective Feedback

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