Designing Inclusive Learning Environments

Brooks, Christopher; Kizilcec, René F.; Dowell, Nia

doi:10.1145/3386527.3405935

Cited by 17 publications

(7 citation statements)

References 19 publications

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“…Within the learning analytics research community, substantial effort has been placed on replication to ensure that models that have been trained and tested with one sample continue to accurately classify members of additional samples. Much of this work has been conducted in the context of massive open online courses (MOOCs; e.g., Brooks et al, 2020; Gardner et al, 2019), including systematic efforts to promote replication (Andres et al, 2018). As more STEM instructors design and repeatedly deliver master courses rich in digital, active learning resources (Olson & Riordan, 2012) in an effort to benefit student learning (Theobald et al, 2020), opportunities to develop prediction models in university courses involving LMSs exist, and similar replication efforts would help ensure the efficacy of these efforts.…”

Section: Discussionmentioning

confidence: 99%

A self-regulated learning analytics prediction-and-intervention design: Detecting and supporting struggling biology students.

Cogliano¹,

Bernacki²,

Hilpert³

et al. 2022

Journal of Educational Psychology

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We investigated the effects of a learning analytics-driven prediction modeling platform and a brief digital self-regulated learning skill training program targeted to support undergraduate biology students identified as likely to perform poorly in the course. A prediction model comprising prior knowledge scores and learning management system log data of student activities during the first 2 weeks in the course was applied to flag students who were likely to earn a C or worse (N = 143). Students who were flagged were randomized into a flagged treatment (N = 79) or flagged control (N = 64) condition. We found that training students who were flagged as likely to perform poorly significantly improved their achievement on unit exams, compared with students who were also flagged but did not receive the training. The effect of training on final examination was mediated by unit exam achievement. In addition, the students who were predicted to perform well (N = 83) and flagged treatment groups did not differ statistically significantly on academic performance. Training also had a significant effect on final course performance with students in the flagged treatment and nonflagged groups outperforming the flagged control students. The results indicate that an algorithm that uses behavioral data to predict achievement does so with sufficient accuracy to detect the large differences in achievement earned by two groups of learners distinguishable by their early, digital learning behaviors, and that a brief $15-minute digital skills training was sufficient to ameliorate these achievement differences when deployed before the first unit exam. Educational Impact and Implications StatementWhen STEM (science, technology, engineering, and mathematics) undergraduates access resources like readings, notes, study guides, and quizzes on the course sites that accompany their lectures, they produce digital traces of learning events. These learning events can be interpreted based on the cognitive and metacognitive processes that the resource affords, serve as proxies for students' internal self-regulated learning processes, and be used to predict the achievement self-regulated learning is known to produce. We used machine learning to build an algorithm based on these digital events that predicted students' exam performance, then provided feedback and training or an additional biology learning activity to those who were predicted to perform poorly, and who were open to receiving learning support. Those who spent around 15 minutes completing a digital, "Science of Learning to Learn" training performed better on exams than students who were predicted to perform poorly but were not asked to complete training and no worse on exams than those who were predicted to perform well. This suggests that the data students produce when learning online can be leveraged to identify those likely to struggle and brief digital training can close the achievement gap predicted by that early pattern of behavior.

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Section: Discussionmentioning

confidence: 99%

A self-regulated learning analytics prediction-and-intervention design: Detecting and supporting struggling biology students.

Cogliano¹,

Bernacki²,

Hilpert³

et al. 2022

Journal of Educational Psychology

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“…As Mercier et al (2023) pointed out, many existing quantitative studies in collaborative learning fail to account for the intersection of multiple groups (e.g., Black + Females in STEM), when minoritized students are aggregated into one group. AI's capacity to detect nuanced learner behaviors enhances the potential of collaborative analytics to model diverse learners at scale, particularly in recognizing and examining intersectionalities (Brooks et al, 2020). These efforts should be at the front agenda for future inclusive collaborative analytics.…”

Section: Assess Skills and Model Learning Processes For Inclusivitymentioning

confidence: 99%

Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity

Nixon,

Lin,

Snow

2024

Policy Insights from the Behavioral and Brain Sciences

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Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students’ experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. Researchers, educators, and policymakers can build an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors.

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“…Strategi pembelajaran diferensiasi diharapkan mampu menciptakan kegiatan belajar dan mengajar yang efektif, memberikan peserta didik kebebasan dalam mendapatkan hakhaknya dalam belajar, serta memfasilitasi kebutuhan belajar peserta didik untuk mengembangkan kemampuannya (Fitriani et al, 2023). Dalam rangka menghadirkan lingkungan belajar yang inklusif dan partisipatif (Beddoes, 2018;Brooks, 2020;Charteris, 2021;Classen, 2022;Costas-Jauregui, 2021;Gill, 2020;Leifler, 2020;Sanger, 2020), beberapa pendekatan juga dapat diimplementasikan seiring dengan strategi pembelajaran diferensiasi. Salah satu pendekatan yang sejalan adalah pendekatan Teaching at The Right Level.…”

Section: Pendahuluanunclassified

Sinergi Antara Pelaksanaan Pembelajaran Berdiferensiasi dengan Teaching at The Right Level dalam Menghadirkan Lingkungan Belajar Inklusif

Prihandini,

Azizah,

Atikah

2023

jtp

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The current learning paradigm has shifted towards student-centered learning. Therefore, teachers need to design learning experiences while considering the characteristics of students comprehensively. This study aims to identify the positive impacts that arise from students and teachers during the implementation of differentiated learning activities with the Teaching at the Right Level (TaRL) approach in the classroom. The research method used is qualitative with a descriptive approach. The research subjects were the 11th-grade students of MIPA 5 at SMAN 1 Tanggul. The observation results indicate that students are more active in learning activities, and their participation in learning is better monitored. Based on the research results presented, it is evident that the implementation of differentiated learning with the TaRL approach has a positive impact on learning and helps create an inclusive and responsive learning environment to meet the needs of the students.

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Designing Inclusive Learning Environments

Cited by 17 publications

References 19 publications

A self-regulated learning analytics prediction-and-intervention design: Detecting and supporting struggling biology students.

A self-regulated learning analytics prediction-and-intervention design: Detecting and supporting struggling biology students.

Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity

Sinergi Antara Pelaksanaan Pembelajaran Berdiferensiasi dengan Teaching at The Right Level dalam Menghadirkan Lingkungan Belajar Inklusif

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