<![CDATA[This research presents a smart learning path recommendation system using Content-Based Filtering algorithm integrated with gamification elements to personalize Indonesian spelling learning for increased student motivation and learning outcomes. Indonesian spelling proficiency remains a significant challenge in higher education, often due to lack of motivation and absence of adaptive learning approaches. While gamification has been shown effective in increasing engagement and personalized learning in improving outcomes, the integration of both approaches with adaptive learning path recommendations remains underexplored in the context of Indonesian language learning. This study proposes an intelligent system that utilizes diagnostic pre-test data to group students into four distinct learner profiles based on their specific spelling weaknesses (e.g., capitalization, punctuation, compound words, abbreviations). The system implements Content-Based Filtering algorithm to dynamically recommend customized learning modules sequenced according to each student's proficiency level. The system was evaluated with 92 participants in a pre-test/post-test design with paired samples t-test analysis. Results demonstrated statistically significant improvement in spelling comprehension (Mean_pre-test = 81.7%, Mean_post-test = 94.6%, t-value (91) = 16.53, p-value < 0.001, Cohen's d = 1.72), indicating an average gain of 12.9 percentage points. Additionally, motivation assessment using the ARCS model (Attention, Relevance, Confidence, Satisfaction) revealed significant increases across all motivation components (Attention: +31.4%, Relevance: +26.3%, Confidence: +40.6%, Satisfaction: +36.1%), confirming that the integrated approach successfully enhanced both learning outcomes and intrinsic motivation. This research contributes to the field by demonstrating the effectiveness of combining algorithm-driven personalization with gamification in language learning contexts.]]>
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