TY - CHAP
T1 - Learning Analytics and Interactive Multimedia Experience in Enhancing Student Learning Experience
T2 - A Systemic Approach
AU - Parra-Valencia, Jorge Andrick
AU - Peláez, Carlos Alberto
AU - Solano, Andrés
AU - López, Jesús Alfonso
AU - Ospina, Johann Alexis
N1 - Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - Learning Analytics (LA) is a feedback loop process that generates data based on learning activities defined by teachers. These data are then stored, adapted, reviewed, and cleaned to derive recommendations to improve the learning experience in an endless cycle. LA has been integrated into user-experience-oriented multimedia systems, and the design of Interactive Multimedia Experiences (IME) can include LA to enhance the learning experience and collect data. However, the efficacy of LA in improving students’ learning experiences remains uncertain with mixed findings from various studies. Therefore, further research is required to evaluate the effects of LA tools on student retention. It is also crucial to consider the correlation between multimedia elements and performance, including the quantity and quality of multi-media features and how they interact with learners’ needs and abilities. It is essential to investigate the effectiveness of multimedia elements in engaging users and their impact on learning outcomes. This chapter proposes the identification of critical feed-back loops that connect the LA process with IME in multimedia projects. The goal was to develop a dynamic hypothesis explaining how the learning experience relates to user experience and teacher enthusiasm. Researchers and teachers will collaborate to identify reference modes, variables, and feedback loops that connect the Learning Experience with the User Experience. By doing so, we can better understand and improve students’ learning experiences by effectively using LA tools and multimedia elements in IME.
AB - Learning Analytics (LA) is a feedback loop process that generates data based on learning activities defined by teachers. These data are then stored, adapted, reviewed, and cleaned to derive recommendations to improve the learning experience in an endless cycle. LA has been integrated into user-experience-oriented multimedia systems, and the design of Interactive Multimedia Experiences (IME) can include LA to enhance the learning experience and collect data. However, the efficacy of LA in improving students’ learning experiences remains uncertain with mixed findings from various studies. Therefore, further research is required to evaluate the effects of LA tools on student retention. It is also crucial to consider the correlation between multimedia elements and performance, including the quantity and quality of multi-media features and how they interact with learners’ needs and abilities. It is essential to investigate the effectiveness of multimedia elements in engaging users and their impact on learning outcomes. This chapter proposes the identification of critical feed-back loops that connect the LA process with IME in multimedia projects. The goal was to develop a dynamic hypothesis explaining how the learning experience relates to user experience and teacher enthusiasm. Researchers and teachers will collaborate to identify reference modes, variables, and feedback loops that connect the Learning Experience with the User Experience. By doing so, we can better understand and improve students’ learning experiences by effectively using LA tools and multimedia elements in IME.
KW - Feedback cycle
KW - Interactive multimedia experience (IME)
KW - Learning analytics (LA)
KW - Learning experience
KW - Multimedia systems
KW - Reference modes
KW - Teacher enthusiasm
KW - User experience (UX)
KW - Variables
UR - http://www.scopus.com/inward/record.url?scp=85183633223&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-40635-5_6
DO - 10.1007/978-3-031-40635-5_6
M3 - Capítulos en libro
AN - SCOPUS:85183633223
T3 - Understanding Complex Systems
SP - 151
EP - 175
BT - Understanding Complex Systems
PB - Springer Science and Business Media Deutschland GmbH
ER -