Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy

Abstract

Computer-assisted instructional programs such as intelligent tutoring systems are often used to support blended learning practices in K-12 education, as they aim to meet individual student needs with personalized instruction. While these systems have been shown to be effective under certain conditions, they can be difficult to integrate into pedagogical practices. In this paper, we introduce three group formation algorithms that leverage learning data from the adaptive intelligent tutoring system ALEKS to support pedagogical and collaborative learning practices with ALEKS. Each grouping method was devised for different use cases, but they all utilize a fine-grained multidimensional view of student ability measured across several hundred skills in an academic course. As such, the grouping algorithms not only identify groups of students, but they also determine what areas of ALEKS content each group should focus on. We then evaluate each of the three methods against two alternative baseline methods, which were chosen for their plausibility of being used in practice—one that groups students randomly and one that groups students based on a unidimensional course score. To evaluate these methods, we establish a set of practical metrics based on what we anticipate teachers would care about in practice. Evaluations were performed by simulating mock groupings of students at different time periods for real ALEKS algebra classes that occurred between 2017 and 2019. We show that each devised method obtains more favorable results on the specified metrics than the alternative methods under each use-case. Moreover, we highlight examples where our methods lead to more nuanced groupings than grouping based on a unidimensional measure of ability.

A review of AI-driven pedagogical strategies for equitable access to science education

“…These platforms analyze students' responses to questions and adjust the difficulty level and content of subsequent questions based on their performance, ensuring that each student receives personalized feedback and support (Hashim, et. al., 2022, Lechuga & Doroudi, 2023, Moleka, 2023.…”

Role of AI in Blended Learning: A Systematic Literature Review

“…In the other seven studies (23.3%), the use of AI technology was found in both the online and offline classroom environments. For example, Lechuga and Doroudi (2022)…”

“…We believe that AI can help instructors control students' autonomy. In the literature, we found that AI was a direct mediator to provide personalized instruction and scaffolding for individual learners (Lechuga & Doroudi, 2022;Phillips et al, 2020). More specifically, an online learning system powered by AI technology assigned repetitive practice (Lu et al, 2021), provided real-time alerts and feedback to prompt students to participate in daily or weekly discussions (Jovanović et al, 2017;Liao & Wu, 2022), and increased the probability of students achieving learning mastery (Phillips et al, 2020).…”

Yapay Zekâ Bağlaminda Okul Li̇derleri̇ni̇ Bekleyen Zorluklar

“…The use of artificial intelligence in education is becoming widespread in different parts of the world under different platforms. For example, some schools use ALEKS (Assessment and Learning in Knowledge Spaces), which has features such as tracking student homework, selfdirected learning, and assessment, and meets a critical need, especially in algebra teaching, as well as in mathematics, chemistry, statistics (Lechuga & Doroudi, 2022;Matayoshi & Uzun, 2022;Phillips et al, 2020). After quickly and accurately determining each student's precise knowledge of a topic, ALEKS helps the student to work on the topics they are ready to learn (ALEKS, 2023).…”