Utilizing learning analytics in course design: voices from instructional designers in higher education
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Utilizing learning analytics in course design: voices from instructional designers in higher education Pauline Salim Muljana1 · Tian Luo1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Studies in learning analytics (LA) have garnered positive findings on learning improvement and advantages for informing course design. However, little is known about instructional designers’ perception and their current state of LA-related adoption. This qualitative study explores the perception of instructional designers in higher education regarding factors influencing their intent and actual practice of LA approach in course design practice, based on analysis of multiple strategies such as focus group, individual, and email interviews. Most instructional designers admitted LA had great potential, but adoption was limited. Their perception, intention, and the current state of adoption are affected by individual differences, system characteristics, social influence, and facilitating conditions. Findings have imperative implications for promoting effective implementation of LA approach in higher education. Keywords Learning analytics · Course design · Instructional design · Instructional designer · Higher education · Technology acceptance model · Phenomenology
Introduction In this digital age, access to technology leaves large digital data footprints. Our world is now a data-driven one, where business and marketing industries analyze customers’ purchasing behaviors using data to predict their interest in future products (Fritz 2011). Amazon predicts the types of books we may want, and Netflix suggests movies according to our favored genres (Dietz et al. 2018). Thus, the potential of data analytics has attracted academia to tap into similar approaches that leverage data. Studies investigating learning analytics (LA) approaches in higher education have garnered positive findings in regard to capturing students’ needs, improving learning * Pauline Salim Muljana [email protected] Tian Luo [email protected] 1
Old Dominion University, 4301 Hampton Boulevard, Norfolk, VA 23529, USA
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outcomes, and supporting student success initiatives (Denley 2014; Dietz-Uhler and Hurn 2013; Gasevic et al. 2016; Smith et al. 2012). Utilizing an LA approach at the course level, instructors can gain insights into students’ learning behaviors based on patterns found within data to inform decisions on needed interventions. These patterns found within data may not be apparent using traditional methods (Muljana and Placencia 2018). In course design practice, LA approach offers benefits for making course-design decisions to meet learners’ needs and enhance learning experience (Dietz-Uhler and Hurn 2013). Research has shown that involving multiple stakeholders, including administrators, instructors, and support personnel like instructional designers (IDs), in the decision-making process is imperative in determining the effectiveness of implementation (De Freitas et al. 2015; Ifentha
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