Students’ feedback experiences and expectations pre- and post-university entry

Gray, Keith; Riegler, Robert; Walsh, Michael P.

doi:10.1007/s43545-022-00313-y

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…In order to enhance students' learning and to ensure a positive impact of feedback on learning, feedback should have specific characteristics that are associated with constructive formative functioning. First, feedback must include comprehensive recommendations for improving the learning task (Gray et al, 2022). Second, feedback should encourage students to reflect on their learning progress, particularly when considering necessary improvement strategies (Zhang, 2022).…”

Section: Feedbackmentioning

confidence: 99%

Experiencing Feedback Channels during Online Research Supervision: A Perspective by Preclinical Students

Nasri

Rahimi²,

Hashim³

et al. 2023

IJLTER

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Due to the unprecedented faculty closures, the COVID-19 pandemic has rendered online learning as one of the most effective tools for maintaining access to higher education. While there is a growing body of literature investigating the pedagogical aspect of online learning, less attention has been paid to address the issue of online research supervision. This qualitative study investigated preclinical students’ perceptions and experiences of different feedback channels during online research supervision. A total of 113 (n=66 females, n=47 males) preclinical students volunteered for the focus group discussions. The data were analysed using a hybrid inductive and deductive analysis approach. Findings revealed three overarching themes: (1) diversified personal input, (2) responsive to socio-emotional needs, and (3) prompt for actions. These findings showed that preclinical students received various feedback through different channels during their online research supervision. Most importantly, this study indicated that personalising feedback channel within an online research supervision serves as the key in supporting and sustaining preclinical students’ research progress, especially during critical times. The findings of this study can be used as a guide for supervisors who are doing remote online research supervision to focus on personalising feedback channels in response to individual students' learning demands and circumstances.

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Section: Feedbackmentioning

confidence: 99%

Experiencing Feedback Channels during Online Research Supervision: A Perspective by Preclinical Students

Nasri

Rahimi²,

Hashim³

et al. 2023

IJLTER

View full text Add to dashboard Cite

show abstract

“…Studies show that the features of quality feedback in higher education are important for all stakeholders. Feedback quality indicators are used by stakeholders in HE to encourage active student participation [71,72]. In studies, it is seen that effective peer feedback enhances self-efficacy, metacognition, and academic achievement in HE.…”

Section: Feedback and Its Purposesmentioning

confidence: 99%

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Hooda

Rana

Dahiya

et al. 2022

Mathematical Problems in Engineering

121

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The core focus of this review is to show how immediate and valid feedback, qualitative assessment influence enhances students learning in a higher education environment. With the rising trend of online education especially in this COVID-19 pandemic, the role of assessment and feedback also changes. Earlier the assessment part is not considered the main focus in learning and teaching in HEIs, but now with the increase in online education, it is observed that the paradigm is shifted toward assessing those activities of students that enhance their learning outcomes. A lot of research work has been done on developing assessment strategies and techniques that can support learning and teaching effectively. Yet, there is limited research that looks at how methods applied in learning analytics can be used and possibly constitutes the assessment process. The objective of this work is to provide an exploratory and comparative study of how assessment and feedback practices can enhance students learning outcomes using AI. The key contribution of this study attempts to capture an outline of the most used artificial intelligence and machine learning algorithms for student success. The results showed that I-FCN performed better than other techniques (ANN, XG Boost, SVM, Random Forest, and Decision Trees) in all measured performance metrics. Also, the result of the comparative analysis study will help the educators, instructors, and administrators on how they could take the advantage of a data-driven approach, design less pressurized, more valid, reliable, constructive assessment findings, and connect the power of assessment and feedback to enhance the learning outcomes.

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“…There have been numerous studies investigating performance feedback's impact on learning outcomes and its effectiveness in enhancing student performance. Feedback is a fundamental aspect of the learning process, playing a crucial role in shaping students' understanding, refining their skills, and fostering continuous improvement (Gray, Riegler, and Walsh, 2022). As Gray, Riegler, and Walsh (2022, pp.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Feasibility and Efficacy of ChatGPT3 for Personalized Feedback in Teaching

Naz,

Robertson

2024

EJEL

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This study explores the feasibility of using AI technology, specifically ChatGPT-3, to provide reliable, meaningful, and personalized feedback. Specifically, the study explores the benefits and limitations of using AI-based feedback in language learning; the pedagogical frameworks that underpin the effective use of AI-based feedback; the reliability of ChatGPT-3’s feedback; and the potential implications of AI integration in language instruction. A review of existing literature identifies key themes and findings related to AI-based teaching practices. The study found that social cognitive theory (SCT) supports the potential use of AI chatbots in the learning process as AI can provide students with instant guidance and support that fosters personalized, independent learning experiences. Similarly, Krashen’s second language acquisition theory (SLA) was found to support the hypothesis that AI use can enhance student learning by creating meaningful interaction in the target language wherein learners engage in genuine communication rather than focusing solely on linguistic form. To determine the reliability of AI-generated feedback, an analysis was performed on student writing. First, two rubrics were created by ChatGPT-3; AI then graded the papers, and the results were compared with human graded results using the same rubrics. The study concludes that e-Learning arning certainly has great potential; besides providing timely, personalized learning support, AI feedback can increase student motivation and foster learning independence. Not surprisingly, though, several caveats exist. It was found that ChatGPT-3 is prone to error and hallucination in providing student feedback, especially when presented with longer texts. To avoid this, rubrics must be carefully constructed, and teacher oversight is still very much required. This study will help educators transition to the new era of AI-assisted e-Learning by helping them make informed decisions about how to provide useful AI feedback that is underpinned by sound pedagogical principles.

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Students’ feedback experiences and expectations pre- and post-university entry

Cited by 7 publications

References 23 publications

Experiencing Feedback Channels during Online Research Supervision: A Perspective by Preclinical Students

Experiencing Feedback Channels during Online Research Supervision: A Perspective by Preclinical Students

Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education

Exploring the Feasibility and Efficacy of ChatGPT3 for Personalized Feedback in Teaching

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