Spiritual Learning Analytics Model Promotes More Inclusive Digital Learning Evaluation for Children with Special Needs

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Jakarta – Digital learning evaluation should no longer focus solely on academic scores or student participation. A study by Afriani Manalu from the Doctoral Program in Christian Religious Education, Universitas Kristen Indonesia, published in 2026, introduces the Spiritual Learning Analytics (SLA) Model, a conceptual framework that integrates authentic assessment, learning analytics, and inclusive education principles. The model is designed to evaluate not only students' academic performance but also their spiritual growth, character development, and learning experiences, including those of Children with Special Needs (ABK). The proposed framework responds to the growing need for digital education systems that are both data-driven and human-centered.

The rapid expansion of digital technology has transformed the way schools and higher education institutions deliver instruction and assess learning outcomes. Learning Management Systems (LMS) now collect vast amounts of educational data, including attendance records, assignment completion, quiz results, and classroom interactions. However, according to Afriani Manalu, these digital evaluation practices remain largely focused on measurable cognitive outcomes while overlooking personal, relational, and spiritual development. At the same time, the increasing demand for inclusive education requires evaluation systems capable of providing equitable learning opportunities for every student, particularly Children with Special Needs.

To address this challenge, the study develops the Spiritual Learning Analytics framework through a qualitative conceptual literature review. The research synthesizes evidence from 23 scholarly sources, including 13 journal articles and 10 academic books. Using thematic and content analysis, the study integrates theories of learning analytics, authentic assessment, inclusive education, and Christian educational theology into a unified model for digital learning evaluation. Rather than proposing another technological tool, the framework offers a comprehensive conceptual approach that places educational values alongside technological innovation.

The findings show that the Spiritual Learning Analytics Model consists of four interconnected components: input, process, output, and outcome, forming a continuous evaluation cycle. The input stage considers students' backgrounds, spiritual development, individual learning characteristics, special educational needs, and technological readiness. During the process stage, authentic assessment, learning analytics, and Universal Design for Learning (UDL) principles work together to ensure fair participation for every learner. The output stage provides a holistic picture of students' cognitive, affective, social, and spiritual development. Finally, the outcome stage focuses on long-term impacts such as faith development, character formation, and the internalization of positive values in daily life.

A major contribution of the study is its emphasis on authentic assessment as the primary source of learning evidence. Instead of relying only on written examinations, educators are encouraged to evaluate students through digital portfolios, reflective journals, community service projects, and multimedia presentations. These learning artifacts provide richer evidence of how students apply knowledge, develop values, and reflect on their personal experiences. According to Afriani Manalu, this approach transforms assessment from a simple measurement activity into a continuous process that supports meaningful learning and personal development.

The framework also expands the role of learning analytics by combining quantitative educational data with qualitative evidence. Student participation records, learning progress, and academic performance are analyzed alongside reflective journals and documentation of spiritual experiences. This integrated approach enables educators to gain deeper insights into students' learning journeys and provide more personalized educational support. At the same time, the study emphasizes that educational data must be managed ethically, with strong protection for privacy and respect for every learner's dignity, ensuring that technology serves as a tool for empowerment rather than surveillance.

Inclusivity represents another key strength of the proposed model. The Spiritual Learning Analytics framework is specifically designed to accommodate Children with Special Needs by integrating assistive technologies such as screen readers, speech-to-text applications, automatic captions, voice recognition, and other accessibility features. These technologies enable students with different abilities to participate equally in digital learning evaluation while demonstrating both academic competence and personal growth through formats that match their individual needs.

According to Afriani Manalu, the Spiritual Learning Analytics Model contributes significantly to educational technology by extending learning analytics beyond academic measurement into the domains of spirituality, ethics, and inclusivity. The framework demonstrates that digital innovation can support learning environments that are reflective, equitable, and centered on human development. It also provides educators with practical guidance for designing digital evaluation systems that strengthen character formation, encourage lifelong learning, and promote meaningful educational experiences for all students.

Author Profile

Afriani Manalu — Doctoral Program in Christian Religious Education, Universitas Kristen Indonesia.

Research Source

Article Title: Spiritual Learning Analytics Model for Digital Learning Evaluation in Christian Religious Education: Integrating Authentic Assessment, Data Analytics, and Inclusivity for Children with Special Needs (ABK)

Journal: East Asian Journal of Multidisciplinary Research (EAJMR), Vol. 5, No. 7, 2026.

DOI: https://doi.org/10.55927/eajmr.v5i7.212

Journal Link: https://journaleajmr.my.id/index.php/eajmr

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