Publications

Feedback is essential in education, yet often studied within isolated educational sectors. This study investigates feedback practices across K-12 and higher education by surveying 254 educators to explore perceptions of effective feedback, student barriers, and methods for tracking feedback impact. Our findings reveal that higher education educators prioritise cognitive and structural feedback elements, emphasising actionable suggestions and timely delivery. In contrast, K-12 educators focus on social-affective aspects, such as feedback tone, to support a nurturing learning environment. Despite these sector-specific preferences, discrepancies exist between educators' beliefs and practices, highlighting a need for enhanced feedback literacy.

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.

Feedback is essential in learning. The emerging concept of feedback literacy underscores the skills students require for effective use of feedback. This highlights students' responsibilities in the feedback process. Yet, there is currently a lack of mechanisms to understand how students make sense of feedback and whether they act on it. This gap makes it hard to effectively support students in feedback literacy development and improve the quality of feedback. As a specific application of learning analytics, feedback analytics (analytics on learner engagement with feedback) can offer insights into students' learning engagement and progression, which can in turn be used to scaffold student feedback literacy. This study proposes a feedback analytics tool, designed with students, aimed at aiding students to synthesize feedback received from multiple sources, scaffold the sense-making process, and prompt deeper reflections or actions on feedback based on data about students' interactions with feedback. We held focus group discussions with 38 students to learn about their feedback experiences and identified tool features. Based on identified user requirements, a prototype was developed and validated with 16 students via individual interviews. Based on the findings, we envision a feedback analytics tool with the aim of scaffolding student feedback literacy.

Background: Feedback is central to formative assessments but aligns with a one-way information transmission perspective obstructing students' effective engagement with feedback. Previous research has shown that a responsive, dialogic feedback process that requires educators and students to engage in ongoing conversations can encourage student active engagement in feedback. However, it is challenging with larger student cohorts. Learning Analytics (LA) provides promising ways to facilitate timely feedback at scale by leveraging large datasets generated during students' learning. However, current LA design and implementation tend to treat feedback as a one-way transmission rather than a two-way process.

Objectives: This case study aims to improve LA design and practice to align with dialogic feedback principles by exploring an authentic dialogic feedback practice at scale.

Methods: We explored a dialogic feedback practice of a course having 700 undergraduate students. The case study used quantitative and qualitative analysis methods to investigate what students expect from feedback, how educators respond to students' feedback requests, and how students experience feedback.

Results and Conclusions: The results emphasise the need to focus on cognitive, relational and emotional aspects of the feedback process. In aligning LA with dialogic feedback principles, we propose that LA should promote the following objectives: reflection, adaption, personalisation, emotional management, and scaffolding feedback provision.

Feedback is an essential process of learning in higher education. Yet, capturing students' interactions with feedback is challenging, which makes it difficult to evaluate its impact. Learning Analytics (LA) is a potential solution to address this issue as it is capable of capturing and analysing learners' activities in a technology-enabled learning environment. LA often use dashboards to deliver insights derived from educational data, yet questions remain on how to most effectively communicate key insights to students. Data Storytelling (DS) is a promising technique to address this challenge by combining data, visuals and narrative to convey key insights. Co-design can facilitate the crafting of visualisations and data stories that best aligns with goals of the students. This study presents the preliminary findings from a design sprint conducted with students to co-design a prototype for a dashboard of an LA solution – PolyFeed – that captures and analyses students' interactions with feedback. In developing the dashboards, students used DS principles – Explanatory titles, Annotations, Highlighting important data points, and Decluttering – to improve the selected visualisations. The results show that the student groups perceived visualising strengths and weaknesses identified in feedback, action plans based on feedback, and trends in their performance as key aspects to include in FA dashboard. However, they primarily used two DS principles: explanatory titles and highlighting key data points to improve visualisations because the dataset was pre-dominantly qualitative. Therefore, the effective use of DS to support qualitative data should be further explored.