Comparative Study of AI-Based Versus Traditional Student Engagement Tracking in Federal Polytechnic, Bauchi, Bauchi State
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- NGN 5000
Background of the Study
Student engagement is a critical factor in academic success, and monitoring it is essential for improving learning outcomes. Traditionally, student engagement has been tracked through manual methods such as attendance records, assignments, and instructor observations. However, with advancements in Artificial Intelligence (AI), institutions are increasingly adopting AI-based systems to monitor and analyze student engagement in real time. These systems utilize data from various sources, including learning management systems, social media, and behavioral analytics, to generate insights into student participation, performance, and overall engagement.
Federal Polytechnic, Bauchi, like many institutions, faces challenges in tracking student engagement accurately and consistently. Traditional tracking methods may overlook key indicators of engagement and are often time-consuming. AI-powered engagement tracking systems can provide a more comprehensive and dynamic approach by collecting and analyzing large volumes of data across multiple channels. This study will compare AI-based student engagement tracking systems with traditional methods at Federal Polytechnic, Bauchi, examining their effectiveness, accuracy, and impact on student performance.
Statement of the Problem
At Federal Polytechnic, Bauchi, traditional methods of tracking student engagement have been inefficient and limited in scope. These methods often fail to capture important aspects of student behavior, such as online participation, late-night study patterns, and emotional engagement with course content. The absence of a more sophisticated system leads to missed opportunities for early intervention when students are at risk of disengagement or underperformance. AI-based systems offer the potential for a more holistic and accurate tracking of student engagement, but their implementation has not yet been evaluated at the Polytechnic. This study seeks to compare AI-based student engagement tracking with traditional methods to determine which approach is more effective in fostering student success.
Objectives of the Study
1. To compare the effectiveness of AI-based student engagement tracking systems with traditional engagement tracking methods at Federal Polytechnic, Bauchi.
2. To evaluate the impact of AI-based engagement tracking on student performance and retention rates.
3. To assess the challenges and benefits associated with implementing AI-based engagement tracking in an academic setting.
Research Questions
1. How does AI-based student engagement tracking compare to traditional methods in terms of accuracy and comprehensiveness?
2. What is the impact of AI-based engagement tracking on student academic performance and retention at Federal Polytechnic, Bauchi?
3. What challenges do faculty and students face in adopting AI-based engagement tracking systems?
Research Hypotheses
1. AI-based student engagement tracking systems provide a more accurate and comprehensive assessment of student participation than traditional methods.
2. AI-based tracking of student engagement improves academic performance and retention rates at Federal Polytechnic, Bauchi.
3. Faculty members and students face significant challenges in adopting AI-based engagement tracking systems.
Significance of the Study
The study will provide insights into the comparative effectiveness of AI-based and traditional student engagement tracking systems. The findings could guide Federal Polytechnic, Bauchi, and similar institutions in adopting AI technologies to enhance student monitoring, improve learning outcomes, and reduce dropout rates. This study will also contribute to the growing body of literature on AI in education, offering practical recommendations for other institutions exploring AI-based solutions.
Scope and Limitations of the Study
The study will focus on Federal Polytechnic, Bauchi, and will compare AI-based engagement tracking with traditional tracking methods within the institution. Data will be collected from students, faculty, and administrative staff involved in the tracking process. Limitations include the availability of accurate engagement data, potential resistance from faculty and students, and the university’s infrastructure for implementing AI-based systems.
Definitions of Terms
• AI-Based Engagement Tracking: A system that uses artificial intelligence to monitor and analyze student interactions with course materials, participation in class, and other academic activities.
• Traditional Engagement Tracking: Methods used by faculty and administration to monitor student participation manually, such as attendance, assignments, and informal assessments.
• Student Retention: The ability of an institution to keep students enrolled until graduation.
• Behavioral Analytics: The use of data to analyze students' actions and behaviors to assess their engagement with academic content.
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