Evaluation and impact planning shifted our focus: what UoR staff and students told us about Blackboard Ultra & how TEL responded

22 June 202622 June 2026 By laurenmccann Comment Closed

An institution-wide evaluation of the university move to Blackboard Ultra, and what the data surfaced about course design, student belonging and inclusion.

By: Jacqueline Fairbairn SFHEA and Emma Herrod SFHEA
Technology Enhanced Learning, Centre for Quality, Support and Development

Excerpt

“Rather than simply evaluating a platform, we found ourselves exploring how everyday Blackboard practices shape the student experience.”

Overview

The University moved every programme onto Blackboard Ultra in 2024/25. The Technology Enhanced Learning (TEL) team used the switch to Ultra as an opportunity to create a more accessible and engaging digital learning experience for students by introducing a new institutional course template, promoting more consistent course design in Blackboard, and encouraging new approaches to teaching and learning across the University.

After the first academic year of use, we wanted to know if the anticipated benefits were realised, so we ran a mixed-methods evaluation with staff and students.

We found it useful to think about our evaluation using the Five Levels of Impact model. Our surveys and focus groups gave us plenty of evidence around Reaction and Learning, whether staff and students found the new features helpful, and whether they felt more confident using Blackboard.

What surprised us was how far the qualitative data pushed us up the evaluation model towards Behaviour. These were changes in confidence, independence, and sense of belonging, particularly for widening participation (WP) students already navigating other barriers. Students talked about greater confidence, independence and belonging, particularly those from groups already navigating additional barriers to learning.

Objectives

To understand how staff and students were experiencing Blackboard Ultra and the new institutional template, beyond simply whether people could use the system.

To find out whether the move to a clearer and more consistent course structure was landing as intended, particularly for WP students.

To use what we learned to shape our Technology Enhanced Learning (TEL) support and guidance, and to feed evidence-informed findings into wider institutional conversations about the role of Blackboard in teaching and learning.

Context

The University is a medium-sized HE institution, so the move to Blackboard Ultra was a significant undertaking with over 27,000 students, around 1,300 academic staff, and colleagues teaching across the UK, Malaysia and China; from a TEL perspective we support more than 2,300 Blackboard courses. All 14 academic Schools moved to Blackboard Ultra at the same time. We supported this with over 160 staff development sessions (650+ staff trained), roadshows across Schools, and a network of Ultra Champions.

Alongside the migration, we introduced a standard institutional course template, built around

a flat structure that limited folder nesting

Ultra Documents to help sequence learning.

These choices aimed to reduce cognitive load, make things easier to find, and support more intentional course design, rather than Blackboard functioning as a file repository.

Implementation

For our evaluation data, we took a mixed-methods approach because we wanted both the breadth of a survey and the depth of user discussions.

Focus group: we met with ten of the university’s student Inclusion Consultants, with a scribe on hand, and asked them to walk through their own Blackboard courses live, narrating what was helpful and what wasn’t.
Written reflections: the same students followed this up with independent written reflections via Padlet, which gave us further material for thematic analysis.
Staff and student surveys: we ran an institutional survey (195 staff, 195 students), combining Likert-scale and open-response questions. We piloted the questions with student partners and our TEL practitioner forum first, which led to some useful revisions around terminology and question ordering.
Platform and accessibility data: we drew on Blackboard’s Ally statistics and worked with our digital accessibility officer and student volunteers to test a sample of courses, focusing specifically on instructor practice rather than the system’s built-in accessibility.

We were interested in three things in particular: how helpful people found Ultra’s features; how well the new template supported navigation and wayfinding, and what people thought of our TEL support and guidance. The aim was to produce a descriptive, exploratory piece of work to surface insights about how Blackboard Ultra was being adopted.

Impact

For instructors; the main headline was that staff confidence in using Blackboard was consistently higher than their confidence in designing Digitally Enabled Learning within it. Under time pressure, a lot of modules were understandably starting out as content repositories rather than properly sequenced learning spaces. Feature usage backed this up:

two-thirds of staff weren’t using Bulk Edit;

half weren’t using the course search bar,

a quarter weren’t using Ultra Documents at all, often because the priority was simply getting content moved across rather than redesigning it.

For students; the picture was more nuanced once we broke it down by widening participation group:

Black, Asian and other minority ethnic students reported particularly strong positive perceptions of the new template, around ten percentage points above the overall baseline on questions about navigation and wayfinding. Their comments suggested Blackboard was helping them navigate academic expectations more broadly, not just locate files.

“From a learner’s perspective, Blackboard functioned as a centralised academic hub, enhancing the coherence and self-regulation of my study practices.”

Commuter students particularly valued features that supported quick, flexible, just-in-time engagement such as announcement pop-ups, a consistent mobile view and the course search bar. For these students, clear structure wasn’t just convenient, it helped them be more strategic with their time. We were struck by how much they wanted to see active lecturer presence in their courses; they liked feeling connected to the module as it unfolded.

“Being able to use my phone and still have the same layout really helps me stay on track when I’m commuting.”

Students reporting a long-term health condition or learning difference told a different story. This group was furthest from positivity averages and showed the greatest sensitivity to inconsistency in layout, file naming and assessment visibility. They valued the system update itself, but inconsistent instructor practice within the course template was amplifying anxiety rather than easing it.

“Overall, I believe the update of Blackboard Ultra to have been a significant factor in terms of easing my anxiety about my studies and examinations this year.”

“Sometimes the assignment info was a bit all over the place, across different [template] sections, which made it difficult to make sure you’d read all the information needed.”

Taken together, the message was consistent (even where the experiences differed); clear, predictable course design changes how confident and connected students feel, and the effect is more strongly experienced by students who are already navigating additional barriers to learning.

Outcomes: what can teaching colleagues do?

The evaluation surfaced a set of small, low-effort changes instructors can make that have a real, positive impact on belonging and the learning journey. None of these require redesigning a module from scratch:

Use meaningful file naming. Keep titles predictable, short and distinct, so students can wayfind easily, including on mobile.
Use the file description field. A brief description helps students orientate themselves and understand what they’ll find in a resource before they open it.
Give a sense of ‘you’. Students value knowing their lecturer is present in the course; a conversational tone, or a short video or audio update, goes a long way.
Combine files in Ultra Documents. Avoid long lists of separate files; use Ultra Documents to guide students through content in sequence.
Aim for anti-scroll Ultra Documents. Keep them short – they should ‘feel’ like an A4 sheet of paper, not an endless scroll.
Uphold consistency. Stick to consistent course structures and templates so students can find information quickly, week to week and module to module.
Schedule release of content and announcements. Timed releases help guide students through the module as it unfolds, rather than leaving everything visible at once.

Reflections

What made the evaluation project useful was resisting the urge to treat it as a checklist of requirements met. The questions that mattered most we not “do people like the updated system”, but “how do everyday practices affect user’s experience”.

If we were doing this again, we would like to find a more systematic way of following up with the WP students whose experiences diverged most, particularly students with a health condition or learning difference, where the gap between intention and instructor practice was most distinct.

One thing we didn’t anticipate was how far the findings would travel. We’d planned this as a local, TEL-focused evaluation piece; however, it opened a wider conversation about institutional approaches to engaging colleagues with Blackboard Ultra practices and ultimately supporting student learning.

Follow up

The findings have since been shared with the University’s Teaching and Learning and Infrastructure Committees, the Awarding Gaps steering group and Reading’s Commuter Student working group, among others. Colleagues presented the work externally at the Blackboard User Group and at the 2026 Durham Blackboard Conference.

Locally, in TEL we will be looking towards:

Developing metrics to monitor adherence to the Ultra template and file-naming conventions, working with School Directors of Teaching and Learning to identify courses needing support.

Shifting more of our TEL support towards course design and digital pedagogy, rather than technical training alone.

Continuing to work with the university groups to shape thinking about how the VLE can better support belonging and connection for students

None of this would have carried the same weight without the evaluation planning behind it. It gave us a thorough evidence base and a student voice to bring into conversations. That’s probably the bigger lesson here; evaluation isn’t just about checking whether a project worked. Done well, it gives you the legitimacy to keep having the conversation afterwards.

References

CQSD Digitally Enabled Learning guide

Reading’s Evaluation and Impact resources

From Prompt to Dialogue: Blackboard AI Conversation Role-plays in Languages

11 June 202622 June 2026 By laurenmccann Comment Closed

Overview

Find out how Spanish Lecturer, Oscar Garcia Garcia, used role-play activities through Blackboard’s AI Conversation tool to support Intermediate and Advanced Spanish III classes, enhancing authentic conversational practice.

Objectives

• Facilitate interaction in Spanish in real-life contexts.
• Practise transferable skills such as negotiation and problem-solving.
• Reinforce and practise the content covered in class through activities.
• Trial the AI Conversation tool in Blackboard for language learning and share findings with colleagues teaching languages in the Department of Languages and Cultures (DLC).

Context

I wanted to trial Blackboard’s AI Conversation tool in two modules to provide students with a new interactive way to practice Spanish in the context of their course content. I chose the two Intermediate Spanish modules I convene, using role-plays based on day-to-day scenarios: a visit to the GP and a negotiation with a landlord/lady to rent a room. I trialled it in Advanced Spanish III with more complex conversations with a task identifying dialectal features and a debate on social media.

Implementation

The implementation of each activity started with identifying the most appropriate sessions in which to introduce it, ensuring alignment with the module content and appropriate timing. I designed each activity carefully, providing clear student instructions and a prompt for the AI with its role, with information on its level of Spanish and background (job, regional origin, age, personality). Before introducing it to my students, I conducted dry runs to test functionality and refine prompts where necessary. In class, I presented the activity and guided students so they could access it, complete it effectively, and understand how the AI Conversation tool works.

Students then completed each task. We did two activities in class: a doctor’s appointment role-play in Intermediate Spanish, and a task identifying dialectal features in Advanced Spanish III. Two other activities – a negotiation with a landlord/lady (Intermediate, scenario shown in the image below), and a debate about social media (Advanced III) – were set for homework.

After completing the two classroom activities, we discussed them; for instance, Intermediate Spanish students had to mention why the patient at the GP was feeling unwell and why they reached their conclusion.

I then proceeded to review their contributions to provide further feedback, draw conclusions, and redesign activities where necessary to improve their effectiveness for my use with future classes. For the two independently completed activities, the process was reversed: I first reviewed the students’ participation in the role plays and subsequently discussed them in class.

Impact

I believe the activities were successful as students engaged effectively with the AI Conversation tool and completed the tasks. The objectives were met, particularly in relation to practicing transferable skills such as negotiation and problem-solving, as well as reinforcing and consolidating classroom content and language skills. I also shared my findings at a departmental TEL event, where it was positively reviewed by colleagues, with a further session planned ti share more widely.

An unexpected outcome was that, although no reflections were required, some students still added brief comments on the usefulness of the activity and what they had learned, which has encouraged me to use the Reflection Question feature in the future in a more effective way.

Reflections

The activities were interactive, motivational and almost game-like, requiring students to solve problems individually or in small groups, or deal with everyday situations in real-life contexts using a language they were learning.They were well received by students and they were closely integrated with the course content. A key factor that made the activity successful was that I instructed the AI to use specific language levels: B1 for Intermediate Spanish and C1 for Advanced Spanish III, aligned with the levels of the respective modules students were studying. This allowed students to put into practice the topics and language, such as grammar and vocabulary, covered in class. I also completed the activity by previewing the chat several times to ensure it worked properly and the AI behaved as expected – I would advise doing this.

The two activities assigned as independent work beyond the classroom had a low completion rate. In the future, they could be carried out in class, or there should be a stronger emphasis on ensuring they are completed. I would also like to use the Reflection Question feature more effectively and clearly, as I consider it very useful for encouraging students to reflect on the activity and its objectives.

Follow up

Since these activities were carried out individually at different points during the academic year, I was able to refine the way I created prompts, making them increasingly concise. In the future, I would welcome the inclusion of voice recognition and pronunciation features within the tool, as these would provide significant benefits for language learning and the development of oral proficiency.

Further information

CQSD TEL guidance: AI Conversations

University of Derby: Enhancing student engagement with Blackboard’s AI Conversation Tool

Coding a fairer system: A new approach to supervisor allocation

28 April 202628 April 2026 By michaelkilmister Comment Closed

By: Katie Barfoot and Anantha Krishna Sivasubramaniam, Psychology & Clinical Language Sciences. Communicating author: katie.barfoot@reading.ac.uk

Decorative image — © Getty Images via Unsplash

Overview

This project transformed a complex, inefficient dissertation-supervisor allocation system for Psychology students into a streamlined, transparent, and fair process. Through workflow redesign, clearer policy, improved communication, and the development of a bespoke allocation algorithm, it significantly reduced staff workload, enhanced student satisfaction, and delivered a more equitable experience for all.

Objectives

Streamline and automate the supervisor allocation process to reduce administrative burden.
Improve fairness, transparency, and consistency in matching students to supervisors.
Increase the proportion of students receiving one of their preferred supervisors.
Enhance student satisfaction and manage expectations through clearer communication and policy.
Develop a scalable, efficient algorithm to support multi-programme allocations.

Context

The activity was undertaken to address the growing complexity of allocating up to 350 final-year Psychology students to dissertation supervisors and project partners across four programmes. Rising student numbers, varied staff capacity, and increasing NSS-driven expectations exposed inefficiencies in the manual process, prompting a need for a fairer, scalable, and more transparent allocation system.

Implementation

The implementation of the student-supervisor allocation process followed a stepwise approach. The first stage focused on streamlining data collection: MS Forms replaced email submissions, allowing students to submit their supervisor choices individually or with project partners. Responses were automatically exported into Excel, reducing administrative burden and minimising errors. A key improvement here was also a shift in workflow. Rather than attempting to manage every stage of the process alone, the module convenor, Katie Barfoot, collaborated with the programme administration team, who brought valuable expertise on sourcing student class lists (RISIS system) and accessing individual student information, allowing the MC to more reliably crosscheck spreadsheet data.

Initially, a published allocation algorithm was applied to match students to supervisors based on preferences and staff capacity. While this automated part of the process, Katie still had to considerably prepare input files and ‘mop up’ output files. Manual oversight was also required for partner matching and in cases of supervisor oversubscription where staff topics were more popular. An outcome of such oversubscription was that students (approximately 5-10% of the cohort) did not receive any of their preferred supervisors and were understandably dissatisfied. Student complaints and requests to change supervisor were also received from students even when receiving one of their choices, due to not getting a first-choice allocation.

We realised how important the student perception of our process was in managing expectations and subsequent (dis)satisfaction. The term ‘ranking’ created a sense of hierarchy, making students feel only a first-choice allocation was successful. To shift expectations, we replaced rankings with ‘preferences’ and asked students to identify six supervisors they would be happy to work with out of a possible thirty, which framed receiving any of their six choices as a positive outcome. A transparent policy was also introduced for requesting supervisor changes. These collective changes were communicated in the project handbook and briefings, and were key to managing student expectations of the process and, in turn, reducing student dissatisfaction. It standardised the process, reduced ad hoc requests, and helped to rebuild student trust.

Although the system was improved, clear challenges remained: the algorithm needed to work across multiple degree programmes with overlapping supervisors – a part that was still being managed manually. Fairness needed to be prioritised within the algorithm over strict optimality, and the number of students missing out on their preferred supervisors needed to be minimised further. Katie reached out to Psychology Research Technician, Anantha Krishna Sivasubramaniam, in the hope that a collaboration could produce a bespoke, future-proofed system that met these objectives.

Anand firstly developed a Jisc Online Survey to collect student responses where the output better integrated with the coding platform, R. Within R, he then developed a single algorithm that sequentially handled all student cohorts (across BSc Psychology, BSc Psychology with Neuroscience, BA Art and Psychology and MSci Clinical Psychology) while automatically updating supervisor capacities. Fairness was built in by randomising the order of students and their preferred supervisors at each allocation step. Students were initially assigned using a ‘greedy fill’ approach: a random student was paired with a random supervisor from their preference list who still had capacity. Any remaining unallocated students were then accommodated in a clean-up step, moving allocated students to another preferred supervisor with capacity to free up space.

Impact

The project successfully achieved its aims of creating a fairer, more efficient, and future-proof supervisor allocation process. Automation significantly reduced the workload for the module convenor and administration team, while the bespoke algorithm enabled 278 students to receive one of their six preferred supervisors (2025/26) – an outcome not previously attainable. Only 45 students who did not submit preferences remained for manual allocation.

Clearer communication and the shift in language from “rankings” to “preferences” helped rebuild student trust and significantly reduced the volume of supervisor change requests compared to previous years highlighting the importance of language within staff-student communications.

Staff confidence also increased due to the introduction of a transparent policy. An unexpected benefit was the smoother collaboration between academic, administrative, and technical teams, demonstrating the value of cross-departmental problem-solving.

Overall, the system reduced manual workload, ensured equitable allocations, and provided a foundation for future improvements – such as fully automating assignments for remaining students – making the process more robust and sustainable for years to come.

Reflections

Several factors contributed to the success of this activity. Early recognition that the existing system was no longer fit for purpose created momentum for change, while close collaboration between the module convenor, programme administration, and the Psychology Research Tech team ensured the solution addressed both pedagogic and logistical needs. Clear communication with students – particularly the shift in language from “rankings” to “preferences” and the introduction of a transparent policy – were essential in managing expectations and rebuilding trust. The bespoke algorithm was a major step forward, demonstrating the value of investing time in tailored technological solutions.

However, implementation could have been strengthened further. Automating the allocation of students who did not submit preferences remains an area for development, as manual assignment persists for this group. More robust integration with existing university systems, such as RISIS, could also reduce reliance on spreadsheet processing. Further, earlier testing with historical data may have helped identify edge cases sooner. Despite these areas for improvement, the project represents a significant and positive transformation.

Follow up

The new algorithm and revised policy have improved fairness, efficiency, and student satisfaction, with fewer change requests. A successful handover to the incoming module convenor has been completed, ensuring the process is transparent, robust, and future-proofed, with ongoing plans to automate remaining allocations and enhance partner matching.

Further information

The R code for our bespoke algorithm is available on request from a.k.sivasubramaniam@reading.ac.uk.

The suitability of personalised AI models for ancient language T&L

10 February 202610 February 2026 By michaelkilmister Comment Closed

By: Jackie Baines and Edward A. S. Ross, School of Humanities; Department of Classics, j.baines@reading.ac.uk; edward.ross@reading.ac.uk

A robot and a human hand almost touching. — Photo by Cash Macanaya on Unsplash

Overview

This article outlines the work undertaken in the Department of Classics to test the effectiveness of GenAI model personalisation to reduce hallucinations and output refining time. These tests found that personalised model using OpenAI’s GPTs, Google’s Gems, and Blackboard Ultra’s AI Assistant made some efficiency improvements, but personalisation had no impact on reducing hallucinations.

Objectives

• To test if personalised GenAI tools can reduce hallucinations related to ancient language vocabulary and reduce the number of required inputs to achieve an expected output, compared to the equivalent freely-available GenAI model.
• To develop ethical and sustainable methods for training personalised GenAI tools.
• To collaborate with students to test the GenAI tools from a learner’s perspective.

Context

Based on previous research on the effectiveness of using GenAI tools to support ancient language T&L, we found that ChatGPT, Google Gemini, Microsoft Copilot, and Claude all frequently outputted hallucinated vocabulary that was not included in the restricted vocabulary lists prescribed in our modules. We found that this would cause problems for students without a firm understanding of their vocabulary requirements, so we sought to determine whether personalised GenAI models would significantly reduce these hallucinations. Furthermore, for sustainability purposes, we hoped that personalised models with pre-prepared guiding prompts would potentially reduce the required number of inputs to achieve an intended output.

Implementation

We developed an exhaustive dataset of all possible Latin words and forms that a student in CL1L1 (Beginners Latin) would be expected to know at the end of the module.
This dataset included 21,825 datapoints and took 48 hours to tabulate.
We prepared personalised models using OpenAI’s GPTs and Google’s Gems interfaces, where we uploaded the datasets and created guiding prompts based on our previous work developing guiding phrases.
Teaching staff and students then tested the personalised models for their effectiveness at supporting ancient language learning in two different tasks: creating and marking vocabulary quizzes and generating additional homework questions.
The personalised model outputs were then compared to equivalent outputs from the general versions of ChatGPT and Google Gemini available at the time.
Teaching staff then tested these same prompts with Blackboard Ultra AI assistant, which only had access to the prepared datasets and CL1L1’s module materials.
Based on the results of these tests, we updated our departmental AI guidance and instructional booklets.
At the beginning of the 2025–2026 academic year, we informed students and staff in the Department of Classics of best practices for supporting ancient language T&L with GenAI ethically and effectively.

Impact

The original intention for this project was to try and reduce the hallucinations present in GenAI outputs related to ancient language vocabulary and thereby reducing the number of prompts required to obtain an accurate desired output. Over the course of this research, we discovered that end-user-friendly GenAI personalisation models are largely ineffective, and sometimes more problematic, when compared to the equivalent general use models. Vocabulary hallucinations were just as persistent in the personalised models as in the general-use models. The major issue, however, was that the personalised models would insist that hallucinated vocabulary was in the original dataset to begin with, while the general-use models would apologize and try to make the mistake a learning opportunity for the user. There was some reduction in the number of required inputs to obtain an accurate desired output, but the hallucination issues tended to outweigh these improvements. For more details about the effectiveness of OpenAI’s GPTs and Google’s Gems, please see Ross and Baines (2025).

Blackboard Ultra’s AI Assistant was able to provide quizzes and extra homework, acting as a tutor. However, despite having access to the vocabulary dataset and module materials, we found that the hallucinated vocabulary issues were also present. When challenged about the presence of unneeded vocabulary, the tool took a balanced approach, compared to the OpenAI and Google’s models.

Screenshot of genAI chatbox — Figure 1. Anthology, Blackboard AI Assistant 3900.121.0, 3 July 2025 version, personal communication, generated 23 July 2025. Prompt: “Write a vocabulary quiz using 2nd declension nouns.”

In the above image (Figure 1), oppidum is a hallucinated noun that is not included in the vocabulary dataset, but the word does exist otherwise. Blackboard Ultra AI Assistant responds to the input that highlights this issue by still providing grammatical details and the opportunity to learn the noun as additional vocabulary. Although this tool does produce the same kind of hallucinations as the other personalised models, it does generate outputs which are similar to a teacher in a classroom.

Reflections

We think that this research is important, despite the lack of positive results. These tests demonstrate that personalisation using general-use AI models like ChatGPT and Google Gemini will not be appropriate for supporting specific language learning tasks, especially for ancient languages. Instead, smaller, independent, bespoke AI models that are trained on restricted datasets would be more effective. However, these models and datasets do not yet exist. Through collaborative work, AI developers and ancient language teachers can create accessible, ethical models to support ancient language T&L.

References and further reading

Ross, E. A. S., & Baines, J. (2025). Navigating the fog: The effectiveness of personalized conversational GenAI models for supporting ancient language learning. AI & Antiquity, 1(1). 35–52. https://doi.org/10.64946/aiantiquity.v1i1.002.
iGAIAS Project Website

Using Padlet in teaching: A reflection on purpose and practice

2 October 20256 October 2025 By michaelkilmister Comment Closed

By: Dr Anna De Amicis, Henley Business School, a.deamicis2@reading.ac.uk

In the years since the pandemic, digital platforms have become a familiar presence in university classrooms. Padlet, Miro and Mentimeter are often presented as ways to boost interaction, make lectures more engaging, and give students new ways to share ideas. But their effectiveness depends less on the technology itself and more on how we, as educators, choose to integrate them.

Why Padlet?

When I introduced Padlet into my social enterprise teaching, my aim was not simply to try out the latest tool. I wanted to encourage more discussion, present concepts visually, and make students’ contributions visible and connected.

Padlet offered a creative space, a digital canvas where students could post images, examples and reflections. It promised to energise the classroom, turning abstract concepts like “mission statements” into something students could critique, rework and co-create together.

What worked (and what didn’t)

In practice, some aspects worked well. When I co-facilitated with a colleague (Jacqueline Fairbairn from CQSD-TEL), the prompt was better received, and students were more responsive. Structured tasks – such as asking students first to find a mission statement, then critique it, then collaborate on improvements – helped scaffold their learning. In these moments, Padlet supported active engagement. Frameworks can help here as shown in the Padlet engagement framework (see Figure 1.1), which sets out clear activity sequences from individual reflection through to group co-creation and evaluation.

Padlet engagement framework diagram showing four structured activity sequences for classroom use. The left side is divided into “Scaffolding individual participation” and “Scaffolding collaborative participation.” Activities include: (1) individual response, (2) paired/group discussion, (3) paired/group co-creation of posts, and (4) peer review. Each activity outlines steps such as posing a question, displaying a joining URL or QR code, and using Padlet for posting and discussion. The right side includes a “Finisher: the plenary” section with guidance on effective wrap-up strategies, including asking open questions and avoiding redirection of posts. — Figure 1.1. .Padlet engagement framework. For an enhanced version with contextualised information, see Fairbairn (2025).

However, there were also times when Padlet fell flat. Used as a quick ten-minute add-on, it disrupted the lecture flow. Students sometimes saw it as “extra work” rather than as part of the lesson. Interestingly, in sessions where we focused purely on facilitation and dialogue, the energy and depth of conversation were even stronger.

Lessons learned

The experience underscored an important point: the value of a digital tool depends on how well it is aligned with the purpose of the session. Students engage most when they clearly see the relevance of an activity and how it links to their learning outcomes.

This is not so much a limitation of Padlet as a reminder for us as lecturers to be intentional. Any classroom activity (whether digital or low-tech) needs meaningful design, clear integration, and facilitation that draws students in. Sometimes, the simplest methods can achieve just as much as a digital platform.

Wider implications

In business education, and particularly in teaching vocational modules, we often emphasise using resources wisely and with purpose. The same principle applies to teaching practice. Innovation in the classroom is not about adopting every new platform but about making deliberate choices that serve our students’ learning.

Digital tools have become part of higher education, and many can be genuinely valuable. But adopting tools because they look innovative is best avoided. What matters is that students experience activities as meaningful, purposeful, and connected to their learning journey.

Reflections

Padlet reminded me of a simple truth: students want to understand the value of what we ask them to do. They respond positively when they see how an activity supports their learning.

So, the real question may not be whether we need Padlet, but how we, as educators, design experiences that are intentional and values led. Technology can play an important role, but only as part of thoughtful, purposeful teaching practice.

References and resources

Padlet & STEM notation: How can you make polls with a program source code on Padlet?

2 October 20256 October 2025 By michaelkilmister Comment Closed

By: Dr Alan Guedes, School of Mathematical, Physical and Computational Sciences, a.guedes@reading.ac.uk

As a computer science lecturer, I am required to show programming source codes in my lectures, particularly for in-class activities like quizzes (e.g. “Determine the output of the following code” or “Identify the logical error within this code”). These quizzes allow me a do an in-class knowledge check for the students.

The recent support for Padlet at the University of Reading prompted me an investigation into an effective approach for displaying a source code in Padlet. However, it became apparent that Padlet is not explicitly designed for this purpose. My initial attempt involved directly placing the source code into a post’s title (image below), but this revealed the limitation of Padlet titles do not support multi-line text. In the following sections, I will share some options I discovered.

Screenshot of a poll creation interface titled “Poll.” The Java code snippet is formatted in a single line with line breaks, showing overloaded add methods and a call to add(2.5, 2). Below the code are multiple-choice options represented by radio buttons arranged in two rows. — Figure 1.1. Pasting source code into the title of a Padlet post

Option 1: Uploading code as a text file

This approach involves using two Padlet posts (image below), where one post is dedicated to an uploaded source code file and other for the poll. The advantage of this method is that it generates syntax colouring for the code, which significantly enhances student readability. However, it presents two drawbacks. First, it requires an additional post solely for the poll, since a single post cannot accommodate both the file and the poll. So, the second post should “refer” to the one before. Second, it suffers from resolution degradation when viewed on mobile devices, what may require students do a second click to “open the file” and improve readability.

Screenshot of a coding question posted by a teacher named Alan Guedes. The question asks, “What is the output of the code below?” followed by a Java code snippet with overloaded add methods for integers and doubles, and a call to add(2.5, 2). Multiple-choice options are 2.5, 2, 4, and 4.5. No votes or comments are present. — Figure 1.2. Using two Padlet posts

Option 2: Code block in the body

This solution involves use only post, which has the code inside its body (Figure 1.3). The post title should remain concise and directly address the quiz query. The source code is then added as a “code block” within the body of the post. This method ensures student readability by preserving indentation and utilizing a monospace font, characteristics common in programming editors.

While this represents a good solution, it has one drawback: it has been observed that Padlet’s default layouts may automatically wrap longer lines of code (Figure 1.4). This can complicate student readability.

Coding question interface showing a multiple-choice question: “What is the output of the code below?” Options are 2.5, 2, 4, and 4.5. The code block includes two overloaded add functions—one for integers and one for doubles—and a call to System.out.println(add(2.5, 2));. — Figure 1.3. Inputting the code block in the body of a post

Screenshot of a coding question displayed in a two-section layout labeled “Section 1” and “Section 2.” Section 1 contains the same Java code snippet and multiple-choice question as Image 1. Section 2 is empty. — Figure 1.4. Code block displayed without text wrapping

To mitigate this issue, the “stream” layout is identified as the optimal configuration. This layout (Figures 1.5 & 1.6) will render content vertically without unnecessary text wrapping, thereby maintaining the readability of the source code.

Screenshot of an application’s appearance settings. Options include wallpaper set to dark, color scheme set to light, font set to Alba, and post size set to standard. Layout format is set to “stream,” with an arrow indicating selection. Group posts by section is toggled on, sorting is set to drag and drop, and new posts appear first. Comments and reactions are enabled. — Figure 1.5. Selecting the text wrapping option

Final remarks

In conclusion, while Padlet may not be inherently designed for showing source code, the file upload and code block options are viable for displaying source code. These methods can be beneficial for lecturers conducting in-class activities involving source code.

If you have a query about the institutional Padlet, please raise a ticket with the TEL Team.

If you’re a University of Reading staff member, you can watch the recording of Alan’s presentation about notation in Padlet by selecting the following link: TEL: Showcase: Power Up your Teaching with Padlet.

ChatGPT: A conversational language study tool

7 April 20258 April 2025 By michaelkilmister Comment Closed

By: Jackie Baines and Edward A. S. Ross, Department of Classics, School of Humanities, j.baines@reading.ac.uk and edward.ross@reading.ac.uk

classical Greek/Roman style columns on a classical ruin with a bright futuristic sky background — Photo by Yusuf Dündar on Unsplash

Overview

This project outlines the work undertaken in the Department of Classics to demystify generative artificial intelligence for ancient language staff and students over the 2023-2024 academic year.

Objectives

Codify and standardise methods for using conversational AI models (such as ChatGPT, Claude-2, and Google Bard) in ancient language classes.
Produce tested guiding phrase documents for students to copy and paste into conversational AI models so that their outputs are standardised to match course expectations.
Lead interactive testing sessions in all levels of ancient language classes (i.e. Latin and Ancient Greek) to test these documents and inform students about the ethical considerations for using generative AI.

Context

At the time when this project was instigated, there was a dramatic surge of generative AI development and use at a generally accessible level. This led to extreme anxiety among educators and students alike as to how these tools could impact the known models for teaching, learning, and assessment in classics and beyond. We sought to approach this issue ‘head on’ in order to contextualise the nature and value of generative AI tools for staff and students, dispelling any unwarranted preconceptions and informing them of necessary ethical considerations.

Implementation

Surveyed ancient language teaching staff about the necessary elements of their courses.
Led sessions with staff to develop the departmental AI guidelines and citation guide in Summer 2023.
Led AI ethics information sessions for all undergraduate and postgraduate students in the Department of Classics over the Autumn 2023 term.
Held survey sessions with all ancient language students studying Ancient Greek and Latin, gathering data on their views on generative AI before and after the information sessions, in Autumn 2023.
Hired three undergraduate research assistants to test guiding phrases on a variety of conversational AI tools to determine the effectiveness of the tools and guiding phrases for supporting various aspects of their ancient language learning.
Published the tested guiding phrases as a digital and physical pamphlet for staff and students to freely use in March 2024.
Recorded and published a series of tutorial videos on generative AI ethics and digital tools for Classics.
Carried out a follow-up survey in Spring 2024 with the same ancient language students that completed the Autumn 2023 survey to gauge the impact generative AI had on their studies over the 2023-2024 academic year.
Analysed and published survey data in two academic journal articles (one is currently in press) and on secure data repositories.

Impact

The initial intention for this project was to investigate how effective generative AI tools were for supporting ancient language teaching and learning, but our research and response from students led us to work more towards improving general AI literacy among humanities teachers and students. When we were giving our ethics presentations, teachers and students were shocked by the ethical considerations behind generative AI, especially the environmental and copyright implications, and as soon as they saw this and learned that their own work could be used to train these models, they were much more sceptical of using the tools. At the time of writing, the tutorial videos and guiding phrase pamphlet have been downloaded around 150 times each, and this continues to grow as we present our tutorials to future course groups.

Reflections

We found this work was successful in many ways, particularly through our collaboration with our undergraduate students. By working with our students, who are seeing large swaths of generative AI tools on a daily basis, we were able to get a wider perspective on the impact and use cases of these tools for ancient language teaching and learning. Any research into generative AI and teaching and learning should involve student-teacher collaboration. In some aspects, we were also interdisciplinary in our reach, making some presentations for the Modern Languages Department, but there is scope for many more interdisciplinary collaborations for this work. In the future, we intend to continue making ethics tutorials for ancient language students, and the materials developed during this TLEP-funded project will help us illustrate the current issues more effectively.

Links

Baines, J., Ross, E. A. S., Hunter, J., McRitchie Pratt, F., & Patel, N. (2024). Digital tools for learning Ancient Greek and Latin and guiding phrases for using generative AI in ancient language study. https://doi.org/10.6084/m9.figshare.25391782.v3
Generative AI ethics and Classics. (2024). YouTube. Retrieved January 9, 2025, from https://youtube.com/playlist?list=PL5m2EUE_vf39iJ97T2jCpOYDfSm0vaGqw&si=ZPYN_zkpQM8jSkd3
Project Website

Overview

This case study explores blending problem-based learning (PBL) and real-world scenarios on foundation economics. By embracing PBL, encourages deep learning, real-world application, and fosters a global perspective in students. In addition to enhancing academic achievement, it prepares students for further academic studies and future professional challenges in a globalised world.

Objectives

To build a deep understanding of foundational economic theories and concepts, while engaging students in practical applications that stimulate critical thinking and problem-solving skills.
The aim is to bridge the knowledge gap for international students who may face challenges transitioning into higher education systems.

Context

On the International Foundation Programme (IFP), we are committed to enhancing the student experience through creative and effective teaching strategies. An innovative approach I introduced on the Foundation Economics modules was the integration of Problem-Based Learning (PBL), in response to student feedback. With an average cohort size of 28, the module offers a supportive and interactive learning environment. Previously, students engaged mainly through quizzes, short-answer tasks, calculations, and textbook-based case studies, with limited real-world application. The shift to PBL marked a significant change, as students tackled real economic issues—such as market failures and government intervention in Microeconomics—through collaborative group work. They researched and critically analysed real-world problems, then proposed possible government intervention/solutions. This approach not only deepened engagement and critical thinking but also developed transferable academic and professional skills aligned with the expectations of higher education and future employment.

Implementation

PBL: Each week, students were presented with real-world economic challenges, such as analysing global trade disputes, inflation crises, or the impact of taxation on different economies. This PBL approach moves beyond traditional lectures, prompting students to work in small groups to research, analyse, and propose solutions using core economic principles.
To encourage more active participation, we adopted the flipped classroom approach. Students access lectures online at home and come to class prepared to engage in discussions, debates, and hands-on activities based on the theoretical content they’ve already absorbed. By shifting lower-order learning to independent study, resulted in more engagement, through interactive tasks. This change enhanced retention of complex economic concepts, and students reported feeling more confident in applying theory to practice.
Cross-cultural perspectives: Given the international nature of the IFP, the module includes case studies from different countries, fostering discussions on how economic principles apply across diverse global contexts. Students were encouraged to draw on their own countries’ economic policies, enhancing relatability and relevance adding unique insights and enriching the learning experience.
Interactive database technologies: Students were introduced to economic databases to analyse trends in GDP, inflation, and unemployment rates. These not only support the application of economic theory but also equip students with skills required in the modern data-driven world.

Impact

– Critical thinking and collaboration: Students developed critical thinking skills by working through ambiguous economic problems in teams -such as the impact of recent inflation on living standards and how this affects students’ daily lives, or the externalities of production and their environmental consequences, encouraging students to propose relevant policy solutions – learning to debate and defend their solutions. Student feedback indicated higher confidence in tackling complex economic problems, a skill crucial for their success in undergraduate studies.

– Global awareness: Students gained a global perspective on economics, appreciating the nuances of economic policies and their impacts in various regions around the world. By creating a learning community where students’ diverse experiences are valued (see Lave and Wenger’s Social Learning Theory, 1991), we fostered a collaborative and inclusive learning environment.

Reflections

Integrating PBL with digital tools (query interface built-in databases – like Statista, ONS and the World Bank Data Portal – transformed the classroom from a space for passive content delivery to a dynamic environment for exploration. One significant benefit was how it catered to different learning styles; students who excelled in independent research complemented those who thrived in group discussions, creating a holistic learning experience. These impacts were evident through classroom observations, where diverse strengths emerged during group tasks, as well as through end-of-seminar student surveys.

While the flipped classroom model was largely successful, there were some challenges, particularly in ensuring all students completed the pre-class materials. To address this going forward, short accountability measures, such as online quizzes, to track progress will be implemented. Some students also required more support transitioning to this self-directed learning model, so additional tutorials will be provided for those who need extra guidance.

I aim to develop a stronger feedback loop, where students regularly reflect on the flipped classroom model’s effectiveness, ensuring that the approach evolves in line with their learning needs.

Follow up

Expanding the use of PBL to cover all major topics in the curriculum
Introducing additional digital tools and simulations to further enrich the learning experience.
Providing continuous professional development for staff to adapt and refine these innovative teaching strategies.

Feedback on the effectiveness of PBL has been collected, and this approach will be incorporated (in seminars) into curriculum areas that students find challenging – identified through their own feedback.

References

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

Enhancing and developing the use of OneNote Class Notebook beyond the Covid-19 pandemic

26 February 202426 February 2024 By michaelkilmister Comment Closed

By: Rita Balestrini, Department of Languages and Cultures, r.balestrini@reading.ac.uk

Overview

The project built on T&L innovation embraced in the Department of Languages and Cultures (DLC), where Microsoft’s OneNote Class Notebook (CN) was trialled during the Covid-19 pandemic to overcome the constraints of teaching languages remotely. The outcomes provided knowledge of DLC students’ experience of CN and understanding of the type of support needed by CN users to staff users of CN, and to colleagues in Technology Enhanced Learning (TEL) and Digital Technology Services (DTS), and informed the development of technical and pedagogical support and guidance for CN users.

Objectives

The project aimed to enhance student learning by improving the use of CN, and had the objective to facilitate sharing of knowledge and expertise, gain insights into students’ experience of CN and inform TEL and DTS decision-making on the tool.

Context

CN is a digital T&L tool to store materials (e.g. text, images, handwritten notes, links, recorded voice, videos), where students and teachers can work interactively in and outside the classroom. It is organised into three parts:

‘Content Library’ – where only the teacher can add, edit, and delete materials.
‘Collaboration Space’ – a place to collaborate in groups open to everyone in the class, where multiple users can work on a page simultaneously or asynchronously.
‘Teacher Only’ – a private space invisible to students.
‘Student Notebook’ – a private area that only a student and their teacher can see and use, where they can interact directly on a page.

CN continues to be used in some language modules as it proved to be effective beyond a remote teaching environment and offered features that supported accessibility and inclusivity in language learning.

screenshot of the classroom notebook — Screenshot of a Class Notebook © Rita Balestrini

Implementation

In 2022–2023, I held three sessions with DLC staff users to share practice and ideas on using CN, and record information on what support would enhance teaching with CN. I also held in-person, small group meetings with DLC CN student users from all year groups to gain insights into their experience of CN. The feedback gathered informed the development of a branched MS Forms survey, which was completed by 28 (of 50) student CN users.
I facilitated a cross-School (DLC, Institute of Education, Law) ‘teaching conversation’ to reflect critically on the pedagogical value of CN.
I wrote a project report for DTS and TEL and shared with them the findings from the needs analysis.

Impact

The project created a ‘space’ for the sharing of practices, knowledge, experience and expertise, which in turn, enabled the enhancement of the use of CN.
It enhanced students’ learning and increased their engagement with CN learning activities – as evidenced by the students’ survey.
As part of ‘internal monitoring and review’ practice, the outcomes of the ‘teaching conversation’, informed the School of Literature and Languages (SLL) T&L enhancement process.
The project should inform the integration of CN with other applications (e.g. Teams), and the provision of technical and pedagogical support for CN users.

Reflections

CN offers a paperless learning environment and facilitates the organisation of T&L materials in a clear and, ‘potentially’ visually intuitive, hierarchical structure. Students evaluated CN positively as a useful ‘digital binder’ and ‘learning tool’ (Average Rating [AR] 4.15 and 4:00 respectively, on a scale of 1 to 5). Most of them felt that materials and resources were easy to access (AR 3.89), and it was easy to take notes within CN (AR 3.74).

CN users generally agreed that navigation in CN is quite ‘fluid’ compared to Blackboard. However, I think that for this fluidity to be fully meaningful pedagogically CN requires a thoroughly thought-out structure, with reasoned and transparent ‘labelling’ throughout the learning environment.

There can be issues in meeting current assessment policies when using CN for summative assessment. However, CN greatly facilitates the provision of feedback with a digital pen or by audio-recording on a page. Staff value the possibility of monitoring individual and group activities and providing private, individualised feedback in different formats; students appreciate highly receiving feedback directly in their personal notebook, which stands out as a noteworthy result of the survey (AR 4.26), especially considering that feedback in general ‘is often framed as the dimension of students’ experience with which they are least satisfied’ (Winstone & Carless, 2020, p. 5).

The ‘Collaboration Space’ can be used for class activities, collaborative projects, sharing resources, and a channel for students’ voice – additional uses of this area depend on the subject taught. CN allows students to take ownership of a shared area and use it for independently chosen purposes, which helps create a sense of ‘community’ and a feeling of ‘online connectedness’ (Hehir et al., 2021).

Regarding technical issues, 68% of respondents did not report any. The others mentioned a variety of problems (e.g. syncing issues, ‘handwriting’ and ‘highlighting’ not anchored to text). Many reported difficulties were linked to using different CN versions, devices, or operating systems, which suggests that improvements could come with advice from technical support specialists.

Follow up

In future, students’ experience of using CN could greatly benefit from:

staff sharing and discussing practices across different subjects to facilitate pedagogical enhancements (e.g., communities of practice, special interest groups, TEL forums);
making access to CN easier and facilitating its integration with Teams and Blackboard;
the availability of expert support from DTS and TEL (e.g. technical assistance, TEL resources and sessions for users).

References

Hehir, H., Zeller M., Luckhurst, J., & Chandler T. (2021) Developing student connectedness under remote learning using digital resources: a systematic review. Education and Information Technologies, 26, 6531-6548. https://doi.org/10.1007/s10639-021-10577-1
Winstone, N., & Carless, D. (2020) Designing effective feedback processes in higher education. Routledge.

Adapting a carousel technique from face to face to remote teaching on Blackboard Collaborate

7 September 202320 September 2023 By michaelkilmister Comment Closed

By: Anjali Mehta Chandar, Charlie Waller Institute, School of Psychology and Clinical Language Sciences (SPCLS), a.m.chandar@reading.ac.uk

Overview

Our vocational postgraduate courses in Low Intensity Cognitive Behavioural Therapy for Children and Young People include a mix of face to face, and remote teaching on Blackboard, at an almost 50/50 split since the COVID-19 pandemic. It is important that trainees engage well with digital methods of learning, and a carousel technique was adapted for Blackboard Collaborate, with hugely positive feedback from the trainees.

Objectives

My objectives for this project were as follows:

adapt the carousel activity from face to face teaching to remote teaching,
gain feedback from trainees about the effectiveness of this strategy and,
make changes as necessary for future implement with other cohorts.

Context

The Educational Mental Health Practitioner (EMHP) programme is a one-year vocational postgraduate course. The trainees are employed by an NHS trust, local authority or charity, and study in the Charlie Waller Institute (within SPCLS) to become qualified mental health practitioners working in primary and secondary schools.

Since the pandemic, trainees have half of their teaching face to face on campus, and half of it via Blackboard Collaborate. Teaching days are 9:30-4:30pm. It has been important, and a learning curve, to ensure online teaching remains as engaging as face to face teaching. It is suggested that a lack of engagement with digitally enhanced learning leads to students being academically disadvantaged (Francis & Shannon, 2013). It is also important that group work is still utilised despite a larger amount of remote teaching than before, as it is an effective method for fostering wider knowledge, clarification on a topic and evaluation of peers’ ideas (Hassanien, 2006).

Carousel activities are an engaging teaching method, and are commonly used in face to face teaching. With this technique, trainees work in small groups to complete an assigned task. Half of the trainees then stay with their projects to explain them, whilst the other half circulate around the room to hear about the other projects. The trainees then switch around, enabling everyone to have a chance to present to their peers, and hear about all the other projects and outcomes too. It was therefore important to try to emulate this for online teaching, as it was a well-received method in face to face teaching.

Implementation

I designed the teaching activity to be 30 minutes of small group work in breakout rooms, and then wanted some trainees to be able to move themselves to three different breakout groups (one every 5 minutes), before moving back to their own original group. Meanwhile the other half would stay in their own group for the first 15 minutes, explaining to their peers about their project ideas, before then moving around 3 groups for the last 15 minutes.

I was able to research how to adapt settings on Blackboard Collaborate to allow this to happen. By clicking ‘allow attendees to switch groups’ when setting up breakout rooms, trainees can move themselves.

Trainees were given strict instructions about the task, including what their project was about based on what group number they were, how to make notes of their project for presenting to their peers after, and how/when to switch groups to hear about other’s ideas. See slides below, which are also available for download as a PowerPoint file:

I was then able to use the chat function to let everyone know to move to the next group (every 5 minutes).

I then closed all breakout rooms after all switches had been made, and we discussed the projects as a whole class.

Impact

Shortly after the carousel task was completed, trainees completed their usual feedback for the teaching day using Menti.

Below is the quantitative feedback about how satisfying, informative and engaging, the day was: (Note: this included other teaching techniques for three quarters of the day, and not just the carousel task in the final quarter.)

I was also able to see qualitative comments about the carousel task in particular, and noted how so many of the comments were specifically about this task. See below:

It seems that trainees appreciated the real-life aspect of the group task, which is in line with research by O’Neill and Short (2023) who found that ”real-world relevant” group tasks in higher education tend to engage students more.

There were only three pieces of feedback about the carousel activity when trainees were asked what could be improved in the day:

“Carousel was a bit chaotic but enjoyed it more than I expected”
“The carousel was not so much fun”
“Felt rushed creating poster and then needing to share”

Whilst the first two are hard to make adjustments for as it is unclear what was not fun and what was chaotic, the third point can be solved by allowing more time for the task itself. However, I am also mindful that the majority of trainees completing the feedback did not state that this felt rushed. To balance this, I can check with trainees on the day if 30 minutes feels like enough time, and go with a majority vote for timing.

Overall, this activity was therefore able to meet its first two objectives:

adapt the carousel activity from face to face teaching to remote teaching, and
gain feedback from trainees about the effectiveness of this strategy.

The third objective was “to make changes as necessary for future implementation with other cohorts.” This has yet to be carried out due to the fact this teaching day resumed to face to face teaching the following year, but a similar technique can be utilised for other teaching days and content.

Reflections

Overall, I feel that the carousel technique was well implemented and only minor changes need to be taken forward. Specifically, this is about ensuring all trainees feel they have enough time for the initial project creation element.

From the feedback, I think trainees enjoyed the task. Clear instructions made this task work well, and trainees enjoyed being able to move themselves around breakout groups. This was perhaps more novel than usual, which may have sparked interest. Similarly, Meng et al. (2019) found that novel techniques in the pharmacy field led to improved learning and communication abilities, and improved academic performance.

Furthermore, the creativity was acknowledged in another section of the Menti feedback –“this was night and day compared to some of the other less creatively organised sessions” – which is in line with research about the importance of creativity in successful and effective teaching in higher education (Bidabadi et al., 2016).

Follow up

Since this teaching, I have noticed how it has not been implemented with other teaching days. For my upcoming allocated teaching, I have mapped out remote teaching days where I can implement this strategy again.

I plan to use the same format for structuring the activities and slides, with the added element of elicited feedback from trainees before the task about whether they feel they have enough time to complete it. I could also enter the different breakout groups and check that they feel they have finished.

I also hope that by disseminating this technique more widely and in more detail than previously – I shared it in the Charlie Waller Institute Teaching and Learning Significant Interest Group – more lecturers will feel able to implement this strategy and gain feedback on future changes to make.

References

Bidabadi, N. S., Isfahani, A. N., Rouhollahi, A., & Khalili, R. (2016). Effective teaching methods in higher education: requirements and barriers. Journal of Advances in Medical Education & Professionalism, 4(4), 170-178.

Francis, R., & Shannon, S. J. (2013). Engaging with blended learning to improve students’ learning outcomes. European Journal of Engineering Education, 38(4), 359-369.

Hassanien, A. (2006). Student experience of group work and group assessment in higher education. Journal of Teaching in Travel & Tourism, 6(1), 17-39.

Meng, X., Yang, L., Sun, H., Du, X., Yang, B., & Guo, H. (2019). Using a novel student-centered teaching method to improve pharmacy student learning. American Journal of Pharmaceutical Education, 83(2), 171-179.

O’Neill, G., & Short, A. (2023). Relevant, practical and connected to the real world: what higher education students say engages them in the curriculum. Irish Educational Studies, 1-18.

Overview

Objectives

Context

Implementation

Impact

Outcomes: what can teaching colleagues do?

Reflections

Follow up

References

Overview

Objectives

Context

Implementation

Impact

Reflections

Follow up

Further information

By: Katie Barfoot and Anantha Krishna Sivasubramaniam, Psychology & Clinical Language Sciences. Communicating author: katie.barfoot@reading.ac.uk

Overview

Objectives

Context

Implementation

Impact

Reflections

Follow up

Further information

By: Jackie Baines and Edward A. S. Ross, School of Humanities; Department of Classics, j.baines@reading.ac.uk; edward.ross@reading.ac.uk

Overview

Objectives

Context

Implementation

Impact

Reflections

References and further reading

By: Dr Anna De Amicis, Henley Business School, a.deamicis2@reading.ac.uk

Why Padlet?

What worked (and what didn’t)

Lessons learned

Wider implications

Reflections

References and resources

By: Dr Alan Guedes, School of Mathematical, Physical and Computational Sciences, a.guedes@reading.ac.uk

Option 1: Uploading code as a text file

Option 2: Code block in the body

Final remarks

By: Jackie Baines and Edward A. S. Ross, Department of Classics, School of Humanities, j.baines@reading.ac.uk and edward.ross@reading.ac.uk

Overview

Objectives

Context

Implementation

Impact

Reflections

Links

Further reading

By: Dr Veundjua Muruko-Jaezuruka, ISLI (International Study and Language Institute), v.muruko-jaezuruka@reading.ac.uk

Overview

Objectives

Context

Implementation

Impact

Reflections

Follow up

References

By: Rita Balestrini, Department of Languages and Cultures, r.balestrini@reading.ac.uk

Overview

Objectives

Context

Implementation

Impact

Reflections

Follow up

References

By: Anjali Mehta Chandar, Charlie Waller Institute, School of Psychology and Clinical Language Sciences (SPCLS), a.m.chandar@reading.ac.uk

Overview

Objectives

Context

Implementation

Impact

Reflections

Follow up