Adaptive Learning Profiles in the Education Domain

Demartini, Claudio Giovanni; Bosso, Andrea; Ciccarelli, Giacomo; Benussi, Lorenzo; Renga, Flavio

doi:10.1201/9781003181187-11

Cited by 1 publication

(2 citation statements)

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“…The comparison ((8) in Figure 2) reveals the difference or error (d i ) between any cluster-related profile and the reference one. This computed error (d i ) drives the feedback-based regulator actions ((3) in Figure 2), which are designed to address and rectify this error [8]. Each cluster ((7) in Figure 2) the sensor generates is associated with specific properties determined through the analysis the algorithms run ((6) in Figure 2).…”

Section: The Data2learn@edu Projectmentioning

confidence: 99%

“…The model architecture chosen in this work underwent validation through pilot trials, utilizing information from the past five years within the educational context [8]. These findings gave educators and administrators insight into the effectiveness of systemic decisions regarding their Learning Organizations, based solely on data collected from and available within their information system repositories [9].…”

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confidence: 99%

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Artificial Intelligence Bringing Improvements to Adaptive Learning in Education: A Case Study

Demartini,

Sciascia,

Bosso

et al. 2024

Sustainability

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Despite promising outcomes in higher education, the widespread adoption of learning analytics remains elusive in various educational settings, with primary and secondary schools displaying considerable reluctance to embrace these tools. This hesitancy poses a significant obstacle, particularly given the prevalence of educational technology and the abundance of data generated in these environments. In contrast to higher education institutions that readily integrate learning analytics tools into their educational governance, high schools often harbor skepticism regarding the tools’ impact and returns. To overcome these challenges, this work aims to harness learning analytics to address critical areas, such as school dropout rates, the need to foster student collaboration, improving argumentation and writing skills, and the need to enhance computational thinking across all age groups. The goal is to empower teachers and decision makers with learning analytics tools that will equip them to identify learners in vulnerable or exceptional situations, enabling educational authorities to take suitable actions that are aligned with students’ needs; this could potentially involve adapting learning processes and organizational structures to meet the needs of students. This work also seeks to evaluate the impact of such analytics tools on education within a multi-dimensional and scalable domain, ranging from individual learners to teachers and principals, and extending to broader governing bodies. The primary objective is articulated through the development of a user-friendly AI-based dashboard for learning. This prototype aims to provide robust support for teachers and principals who are dedicated to enhancing the education they provide within the intricate and multifaceted social domain of the school.

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Section: The Data2learn@edu Projectmentioning

confidence: 99%

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confidence: 99%