Toward Trait-Aware Learning Analytics
- URL: http://arxiv.org/abs/2602.00018v1
- Date: Fri, 16 Jan 2026 01:17:27 GMT
- Title: Toward Trait-Aware Learning Analytics
- Authors: Conrad Borchers, Hannah Deininger, Zachary A. Pardos,
- Abstract summary: We argue for an expanded framing of learning analytics (LA) that centers on learner traits as key to both interpreting and designing experiments.<n>We show that personality traits are relevant to LA's central outcomes and conducive to action.<n>We propose that LA can evolve by treating traits not only as predictive features but as design resources and moderators of analytics efficacy.
- Score: 2.6998665629152536
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Learning analytics (LA) draws from the learning sciences to interpret learner behavior and inform system design. Yet, past personalization remains largely at the content or performance level (during learner-system interactions), overlooking relatively stable individual differences such as personality (unfolding over long-term learning trajectories such as college degrees). The latter could bring underappreciated benefits to the design, implementation, and impact of LA. In this position paper, we conduct an ad hoc literature review and argue for an expanded framing of LA that centers on learner traits as key to both interpreting and designing close-the-loop experiments in LA. We show that personality traits are relevant to LA's central outcomes (e.g., engagement and achievement) and conducive to action, as their established ties to human-computer interaction (HCI) inform how systems time, frame, and personalize support. Drawing inspiration from HCI, where psychometrics inform personalization strategies, we propose that LA can evolve by treating traits not only as predictive features but as design resources and moderators of analytics efficacy. In line with past position papers published at LAK, we present a research agenda grounded in the LA cycle and discuss methodological and ethical challenges.
Related papers
- Towards Agentic Intelligence for Materials Science [73.4576385477731]
This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
arXiv Detail & Related papers (2026-01-29T23:48:43Z) - SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking [23.78415242490134]
Social cues play a crucial role in human information seeking by helping individuals judge relevance and trustworthiness.<n>Existing LLM-based search systems rely on semantic features, creating a misalignment with the socialized cognition underlying natural information seeking.<n>We propose design implications emphasizing better social-knowledge understanding, personalized cue settings, and controllable interactions.
arXiv Detail & Related papers (2026-01-03T07:09:10Z) - A Review of Developmental Interpretability in Large Language Models [0.0]
This review synthesizes the nascent but critical field of developmental interpretability for Large Language Models.<n>We chart the field's evolution from static, post-hoc analysis of trained models to a dynamic investigation of the training process itself.
arXiv Detail & Related papers (2025-08-19T18:19:16Z) - PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses [5.750960656720476]
The PAnoramic Learning Map (PALM) is a learning analytics (LA) dashboard designed to address the scalability challenges of LA.<n>We conducted a system evaluation to assess PALM's effectiveness in two key areas: (1) its impact on students' awareness of their learning behaviors, and (2) its comparative performance against existing systems.
arXiv Detail & Related papers (2025-07-24T13:17:47Z) - Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning [67.90033766878962]
Self-supervised feature learning (RL) often rely on information-theoretic principles, termed mutual information skill learning (MISL)<n>Our work investigates MISL through the lens of identifiable representation learning.<n>We prove that Contrastive Successor Features (CSF) can provably recover the environment's ground-truth features up to a linear transformation.
arXiv Detail & Related papers (2025-07-19T20:48:46Z) - Illusion or Algorithm? Investigating Memorization, Emergence, and Symbolic Processing in In-Context Learning [50.53703102032562]
Large-scale Transformer language models (LMs) trained solely on next-token prediction with web-scale data can solve a wide range of tasks.<n>The mechanism behind this capability, known as in-context learning (ICL), remains both controversial and poorly understood.
arXiv Detail & Related papers (2025-05-16T08:50:42Z) - The ISC Creator: Human-Centered Design of Learning Analytics Interactive Indicator Specification Cards [0.0]
We present the systematic design, implementation, and evaluation details of the ISC Creator, an interactive learning analytics tool.<n>Our findings demonstrate the importance of carefully considered interactivity and recommendations for orienting and supporting non-expert LA stakeholders to design custom LA indicators.
arXiv Detail & Related papers (2025-04-10T14:49:47Z) - Evaluating Large Language Models with Psychometrics [59.821829073478376]
This paper offers a comprehensive benchmark for quantifying psychological constructs of Large Language Models (LLMs)<n>Our work identifies five key psychological constructs -- personality, values, emotional intelligence, theory of mind, and self-efficacy -- assessed through a suite of 13 datasets.<n>We uncover significant discrepancies between LLMs' self-reported traits and their response patterns in real-world scenarios, revealing complexities in their behaviors.
arXiv Detail & Related papers (2024-06-25T16:09:08Z) - Decoding In-Context Learning: Neuroscience-inspired Analysis of
Representations in Large Language Models [5.062236259068678]
We investigate how large language models (LLMs) exhibit remarkable performance improvement through in-context learning (ICL)
We propose novel methods for parameterized probing and measuring ratio of attention to relevant vs. irrelevant information in Llama-2 70B and Vicuna 13B.
Our analyses revealed a meaningful correlation between improvements in behavior after ICL and changes in both embeddings and attention weights across LLM layers.
arXiv Detail & Related papers (2023-09-30T09:01:35Z) - Sensitivity, Performance, Robustness: Deconstructing the Effect of
Sociodemographic Prompting [64.80538055623842]
sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give.
We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
arXiv Detail & Related papers (2023-09-13T15:42:06Z) - AI Text-to-Behavior: A Study In Steerability [0.0]
The research explores the steerability of Large Language Models (LLMs)
We quantitatively gauged the model's responsiveness to tailored prompts using a behavioral psychology framework called OCEAN.
Our findings underscore GPT's versatility and ability to discern and adapt to nuanced instructions.
arXiv Detail & Related papers (2023-08-07T18:14:24Z) - Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal.
In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
arXiv Detail & Related papers (2021-01-19T12:23:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.