ECR Research Grant
Funding Guidelines
Purpose
The SoLAR ECR Research Grant serves to support promising early career researchers who demonstrate the potential to advance research and practice in learning analytics and increase the educational and socio-economic impacts of learning analytics. Proposed projects should improve our understanding of learning analytics and learning in general, result in positive impacts on education, and align with the topics identified for the current round. It is expected that the ECR grant will help successful applicants develop their career by enabling an initiative that is scalable or has potential to attract further funding.
Applicant eligibility
Applications from individuals based in academic or not-for-profit organizations are welcome, provided the individuals hold a valid SoLAR membership at the time of application and throughout the project duration. Applicants need to have been awarded with a PhD qualification no longer than 5 years prior to the application deadline. Each applicant may only submit one application, and only individual applications are allowed. Past awardees or current SoLAR executive committee members are not eligible to apply.
2025 Themes - Human-centred learning analytics and AI: Shaping the future of learning
Without a doubt, AI, especially generative AI, has continued to proliferate within the educational landscape. Research in our field has seen a burgeoning increase in works proposing tools leveraging generative AI in particular, as part of students’ learning. However, while such tools present exciting opportunities for student learning (e.g., Deng et al., 2024), and sit at the cutting edge of such technologies, these tools are starting to come under scrutiny with respect to whether they do in fact support critical aspects of student learning. Within higher education in particular, research is emerging to indicate that Generative AI tools may impair students’ self-regulated learning (e.g., Darvishi et al., 2024; Fan et al., 2024). In view of these emerging findings, it is important to return to the notion of human-centredness in regard to learning analytics and AI tools.
In this year’s SOLAR ECR Grant call, we welcome proposals of research that showcase the practicalities, possibilities and challenges in promoting human-centred learning analytics and AI, in order to ensure that tools grounded in these approaches are fit for purpose, meet stakeholders’ needs, and most importantly, foster student agency and learning. Example topics include, but are not limited to:
- Understand the impact of AI technologies on student learning and teaching;
- Identify human-centred AI driven methodologies and tools to gain insights into learning;
- Understand how AI technologies impact different strands of learning analytics research, e.g., personalised feedback, writing analytics research, self-regulated learning, etc.;
- Explore how learning analytics can be used to foster insights into new AI literacies, such as prompt engineering;
- Explore the role of learning analytics in futures literacy;
- Examine how generative AI can be used together with learning analytics in learning design;
- Make sense of the intersections of humans and AI in practices of learning analytics;
- Enhance the use of learning analytics with explainable AI;
- Evaluate the impact of learning analytics on institutional strategy development and monitoring;
- Explore how institutions navigate ethical considerations of AI when adopting learning analytics;
- Explore how to ensure trustworthiness of learning analytics and AI in education in parallel with data protection and informed consent of data owners;
- Understand ways that algorithmic fairness & biases in AI technologies may affect learners;
- Explore ways learning analytics may incorporate AI to tackle major challenges in education, such as educational equity and quality.
Level of award
A total of $15,000 CAD is available to fund one project. Funds will be managed directly by SoLAR via approved expense invoices.
Awardee(s) will also receive publicity via SoLAR’s channels to help them connect with the community, complimentary registration for a regional Learning Analytics Summer Institute (LASI) or LAP event, and the International Learning Analytics and Knowledge Conference (LAK).
Eligible costs include:
- Direct costs of research (e.g., consumables, specialist software, transcribing/ interpretation services, and compensation for research participants). Note that the following items are not eligible costs: institutional overheads, computer hardware, books and other permanent resources.
- Research assistance - Funding can be used to pay for research assistants that are not named on the project based on SoLAR’s fixed hourly rate (i.e. $30USD) under an agreed total flat amount. Awardees may utilise SoLAR’s communication channels for job advertisements.
- No more than a third of the awarded amount of funding can be used for dissemination purposes including publications, conferences, and workshops.
- Visits by or to partners of the project to facilitate research activities or dissemination that meet a clearly specified research objective.
Note that successful applicants will be given a spotlight moment at LAK to describe their work, and will be given a poster slot to report results. Travel and accommodation costs to attend LAK and LASI to present their results can be included in the budget if applicable.
Period of award
Grants are tenable for 12 months from the date of award. Unused funds will expire after this period and cannot be claimed.
Selection process and criteria
Applications will be assessed by external reviewers based on the following criteria:
- Project quality and innovation
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- Key contributions to existing gaps in knowledge or problems
- Novelty and originality of the project
- Clarity, coherence, and rigour of the research design
- Project feasibility
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- Project design and budget planning are reasonable for successful completion of the project and demonstrating value for money
- The capability of applicants and availability of facilities needed to carry out the project
- Project benefits and impacts
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- Contributions to the field
- Social, cultural, and economic impacts on society
- Scalability and sustainability of impacts
- Project relevance to the topics prioritised in the call
- Applicant profile
- Relevant expertise and experience required to carry out the project
- Academic achievements and output quality
- Commitment and contributions to the learning analytics community
Expectations of awardees
Awardees are required to:
- Submit an interim report on project progress no later than sixth months from the date the grant is awarded.
- Submit a research report no later than three months from the completion of the project detailing outcomes of the work undertaken and next steps.
- Disseminate project outputs via one of SoLAR’s existing communication channels (e.g., Nexus blog, webinar, SoLAR Spotlight podcast, and Journal of Learning Analytics) as appropriate in support of SoLAR’s mission to promote learning analytics research and practice.
Application guidelines
Submission procedure
Please complete the online application form, which includes the following components:
- Applicant information
- Project title
- Project summary (1250 characters max)
- Project fit for the themes of the year (1000 characters max)
In the application form, please upload one PDF file containing:
- Max 4 pages: Project description (including title, summary, aims and objectives, research scheme, methods, timeline, budget, ethical procedures, expected outcome and impact, and project fit for the themes of the year)
- Max 2 pages: CV of the applicants including relevant publications
In addition, a recommendation letter (1 page max) needs to be e-mailed by the referee directly to info@solaresearch.org before the application deadline.
Key dates
Applications open: Friday 21 March 2025
Submission deadline: Monday 19 May 2025 (11:59pm AoE)
Results announcement: mid-July 2025
Shamya Karumbaiah, University of Wisconsin-Madison
2024 Recipient
Title: Characterizing Biases in Large Language Models from Students’ Linguistic Variations
Project summary:
Large language models (LLMs) herald new possibilities in learning analytics for developing task-specific predictive models (e.g., for automated assessment and feedback) using general-purpose models without annotated data or model training. However, a limitation of using LLMs in high-stakes settings such as ours is the lack of rigorous evaluation for the downstream task. Recent research on bias in learning analytics has also shown the harms of not evaluating models within a deployment context and population (e.g., multilingual students). Current methods to audit for algorithmic bias fall short with LLMs due to the lack of substantial annotated data, let alone data from diverse student populations. To address this challenge, we aim to develop a new conceptualization of LLM bias as a consequence of linguistic variations in students’ writing. The project makes methodological contributions to responsible and trustworthy use of LLMs in learning analytics by: (1) showing how LLM bias manifests in relation to linguistic variations and how that bias can accumulate across performances, 2) guiding stakeholder and researcher investigations into bias and its consequences, and 3) informing efforts to reduce bias where detected.
The 2024 theme acknowledges two trends that align with goals of the proposed research: 1) growing interests in AI, especially generative AI (e.g., LLMs) in education, and 2) growing concerns on ethical considerations and challenges of inclusion (e.g., multilingual learners). The proposed approach to conduct this research also aligns well with the two goals of the funding: 1) grounding the needs and contextual complexities of historically marginalized students, and 2) methodological research the two fields of research together, that is, learning analytics and AI (i.e., LLMs). The following topics are particularly relevant to the proposed project: 1) understand ways that algorithmic fairness & biases in AI technologies may affect learners, and 2) explore ways learning analytics may incorporate AI to tackle major challenges in education, such as educational equity and quality.
Yizhou Fan, Peking University
2023 Recipient
Title: Measuring and Scaffolding Hybrid Human-AI Regulation: Comparing Learning Processes Facilitated by ChatGPT and Human Experts
Project summary:
The advances in AI have radically and will continually change the workforce by automating many jobs in all walks of life. Therefore, it is vital for students and professionals to be able to learn and work with AI, which has been an increasingly central focus of education. As the practice and research of AI-assisted learning emerge, a new evolution for learning analytics is to measure and understand how learning takes place with the scaffolding of AI. However, relevant empirical research is still in an early stage, and further exploration is urgently needed. Therefore, to fill this gap and investigate how AI could better facilitate learning, I propose this project to compare ChatGPT with human experts and reveal how practical AI could promote learners’ regulation process and consequently improve their performance. I will conduct a lab experiment, recruit 90 participants, and randomly assign them to three conditions. During the project, learners’ multi-channel data, such as pre-post-test, survey, learning trace, and interview data, will be collected and analysed using cutting-edge learning analytics approaches and methods. This project will illuminate how to enhance learning with learning analytics in an AI-powered world.
This year's theme is enhancing learning with learning analytics in an AI powered world. Here, I focus on measuring and scaffolding hybrid human-AI regulation using cutting-edge learning analytics approaches (e.g., trace SRL) and promising methods (e.g., ONA). Mainly, this project will design and evaluate an AI-driven scaffolding tool which uses ChatGPT to provide learners context-specific facilitation in a challenging task. By comparing learners' interaction with ChatGPT and human experts, this project will reveal how and to what extent AI could impact learning and thus unpack how learning with AI takes place. Furthermore, this project will examine how new generational AI impacts learning and assessment by comparing learners' regulation processes and their performance across different conditions. Finally, the proposed project will also explore how learners could interact with AI more efficiently, which can be used to foster insights into new AI literacies, such as prompt engineering.
Catherine Manly, Farleigh Dickinson University
2022 Recipient
Title: Building an Institutionally Workable Prescriptive Analytics Approach to Support Tutoring Recommendations and Course Redesign
Project summary:
This project aims at closing the performance gap for students across the full range of dis/ability, whether disclosed or not, even when efforts to anticipate and design for student learning requirements (such as through a Universal Design for Learning (UDL) approach) fall short and students struggle to learn course material. In doing so, this project utilizes an atypical analytics approach, using prescriptive analytics to project predicted potential outcomes for individual students in different simulated worlds, allowing comparison of hypothetical future scenarios in order to make recommendations to students in the present. With additional potential to inform course redesign efforts by identifying places in the course where students are struggling, this project will involve faculty, staff, and students at Bay Path University in action research to collaboratively determine the next steps involved in institutionalizing this approach. Focus group data will inform development of a pilot for integrating prior proof-of-concept work with the data warehouse. The outcome will be a feasibility analysis report regarding potential implementation. Results will provide an example of how prescriptive analytics could be more broadly implemented.
This project fits squarely within the Supporting Inclusive Learning with Learning Analytics theme. The UDL framework underpinning this work aids course developers designing learning experiences with learner variability in mind, making these experiences accessible to students with and without disabilities, such as in affective or cognitive dimensions. Creating learning experiences that are enabling for all students holds potential to minimize gaps that might otherwise be caused by disabling aspects of the learning environment. By pursuing this action research in collaboration with faculty, staff, and students, the project aims to better understand contextual factors that will facilitate co-designing a prescriptive analytics system. The university’s innovative culture has already fostered development of predictive and learning analytics, particularly in the context of adaptive learning (Anderson & Bushey, 2017), priming it to extend this work to prescriptive analytics.
ECR Grant FAQ
A: No. Each person can only be named on one application, and each application can only include one applicant.
A: No, learning analytics is an interdisciplinary field. We are open to any methods that are suitable to answer the questions at hand, provided a rigorous and ethical procedure will be followed.
A: It will depend on the length and justification of the claimed career interruption. Please email info@solaresearch.org to clarify.
A: Yes, we accept proposals from anywhere in the world. Application materials must be submitted in English and project budgets must be in Canadian dollars. Applicants also need to hold valid SoLAR membership at the time of application and during the period of the project term.
A: SoLAR ECR research grant is an individual researcher grant. As long as the proposals are for different projects from different individuals, it is fine for multiple applications to be submitted from the same university.
A: No. The SoLAR ECR Research Grant is meant to enable an initiative that may help an ECR to start their own team and work towards a larger project. We would reserve the opportunity for those that have not received such support from SoLAR yet.
A: Yes. However, resubmissions are considered among all of the other newly submitted proposals and are not given special status or consideration in the review process.
A: A start date between 2 -3 months of proposal deadline is recommended. SoLAR will manage directly the Research Grant and will disburse funds as required upon submission of SoLAR Expense forms that will be provided to the successful awardee. Funds will not be disbursed without the approved signed forms. Once received payment will be made within 7 working days.
A: In-kind giving or cost sharing is not expected or required as part of your proposal budget. However, if you plan to include in-kind giving or cost sharing as part of your project budget, you should indicate this in the budget section. If your proposal is chosen for funding, the grant award may be contingent upon receiving documentation confirming the additional support.