Call For Papers

Marrying Asset- and Deficit-Based Approaches: A Data Feminist Perspective in Learning Analytics

General: This workshop considers how learning analytics can marry deficit- and asset-based approaches.  In this context, we refer to deficit-based approaches as those that emphasize what learners “lack,” how their performance “fails” to attain normative standards, or “gaps” between learners and their peers. Deficit models, as described in this context, pinpoint areas that need intervention. This "find and fix" approach frequently enhances analytics, helping guide both students and teachers while also informing necessary interventions or adaptations.

While this approach has its merits, it also has drawbacks. Deficit-based methods can be restrictive in interpreting data, thereby reducing learning analytics' potential benefits. Such framing focuses on "fixing" students identified to be “lacking” in some regard, neglecting to recognize or utilize their inherent strengths, skills, and strategies. Furthermore, this approach can, in some cases, implicitly communicate to students (and other stakeholders) that certain assets do not matter. Asset-based approaches highlight learners' existing knowledge and strengths, derived from education, culture, or personal experiences. In this workshop, we advocate blending this with proven "find and fix" solutions, emphasizing the recognition of students' assets.

Notes about workshop: In this workshop, we are not referring to a cultural deficit perspective which posits that students from certain groups cannot achieve due to their cultural background. The deficit view overlooks broader social inequalities faced by diverse student groups, and can have unethical side effects within learning analytics.

Additionally, we guide this workshop using the data feminism framework, which takes an intersectional approach to defining the power structures involved in designing, collecting, and interpreting data. Data feminism posits that data is not neutral, and encodes elements of our identity and cultural experiences (both of those designing data collection methods, and those from whom data is collected). Particularly relevant for our context, this framework also highlights the ways data can be used to construct narratives that challenge both power structures and our understanding of students. 

Paper Submissions

We invite papers that reflect not only on new empirical data or past experiences on this topic, but also speculate on the future of Learning Analytics. Topics of papers can include: 

• Methods for marrying asset- and deficit-based narratives 

• Challenges experienced when attempting to take asset-based approaches

• Core considerations to incorporating asset- and deficit-based narratives of learners

• Speculative futures of Learning Analytics as it relates to asset- and deficit-based narratives

• Theoretical and methodological issues for Data Feminism in the context of Learning Analytics

• Lessons learned from existing asset-based learning analytics

• Examples of data feminism principles as applied to Learning Analytics 

• Opinions on the state of Learning Analytics if the core principles of Data Feminism, and married asset- and deficit-based narratives are achieved

Submission Instructions 

Submissions should be at least 500 words. There is no maximum length.

Please note all papers selected for presentation during the workshop will NOT be included in the LAK companion proceedings this year. Independent proceedings shall be submitted to CEUR-WS.org for online publication, but this is voluntary for authors.

Submissions can be made via the submission tab

The deadline for submission is 16 Dec 2023 11:59pm AoE

Notification of acceptance will be sent no later than January 13th 2024