Open and distance courses provide learners different contexts and settings from face-to-face environment. These differences have also transformed the characteristics of pedagogy in open and distance learning environment (Gros, Suárez-Guerrero, & Anderson, 2016), among which are self-regulation skills of learners, learner activities and quality of interaction (Cerezo, Sánchez-Santillán, Paule-Ruiz, & Núñez, 2016; Friensen & Kuskis, 2013). Thus, online learning experiences need to be analyzed based on interaction data in online learning.

Learning experiences of learners should be effective and optimized to have high learning performance in online learning. What is important at this point is to determine which of those indicators could be investigated to better understand their learning experiences. Interaction data derived from LMS logs, for example, have also been used as essential indicators of online learning experiences in the field of learning analytics (You, 2016). The interest in analyzing educational data over the past decades have highlighted learning analytics as a significant research field. The major goal of the learning analytics is to understand and improve student learning thorough making actionable knowledge and providing meaningful feedback to students and teachers. It can be said that learner interaction data provide a wealth of information about learning process (Doleck, Basnet, Poitras, & Lajoie, 2015; Joksimović, Gašević, Loughin, Kovanović, & Hatala, 2015). Thus far, a number of studies have revealed significantly an association between interaction patterns of learners and online learning outcomes (Akçapınar, Altun, Aşkar, 2015; Cerezo et al., 2016; Li & Tsai, 2017; You, 2016). For example, Cerezo et al. (2016) concluded that different patterns of interaction with the LMS was associated with learning performances of learners. Similarly, Li and Tsai (2017) found that three behavior patterns which were clustered based on learners’ viewing behaviours, were also associated with learning performances. In another study, You (2016) revealed that there is a strong relationship LMSs interaction data and learning performance. Thus, more insight into interaction pattern indicating learning behaviours of learners is essential to understanding effective online learning experience in LMSs considering other personal traits of learners.

To build effective learning experiences, psychological traits and cognitive/non-cognitive difference of individiuals should be considered in designing open and distance learning environment and learning analytics dashboards. Polychronic–Monochronic Tendency of learners is one of the prominent variables which have an effect on individual differences of learners (Kaufman‐Scarborough, 2017). Lindquist and Kaufman-Scarborough (2007, p.269) defined as polychronicity as a form of behavior wherein a person engages in two or more activities during the same block of time, while monochroncity occurs when a person engages in one activity at a time. To the best of our knowledge, there is a limited research on relation between Polychronic–Monochronic tendency and interaction patterns of learners and very little is known about relation between Polychronic–Monochronic tendency and interaction patterns in open and distance learning environment using learning analytics dashboards. This study aims to contribute to this growing area of research by exploring understanding of the relationship between Polychronic–Monochronic tendency and online learning experiences of learners in the context of learning analytics.

The research was carried out in a 14-week-long Research Methods course offered in the fourth year for students enrolled in an Instructional Technology program. The course was designed using Moodle v.3.5 open source learning management system. The students were also provided an in-house designed and developed a prescriptive learning dashboard embedded in the Moodle platform. Five measurable metric values were extracted by cleaning and pre-processing raw log data. These metrics are indicators of time spent on learning resources and frequencies of learners’ access to the prescriptive learning dashboard. To measure learners’ Polychronic–Monochronic tendency, the five-item summated scale named the Polychronic–Monochronic Tendency Scale was used (Lindquist & Kaufman-Scarborough, 2007). The data were analyzed using educational data mining techniques and prediction methods.

The preliminary findings show that there is a significant relationship between learners’ Polychronic–Monochronic tendency and interaction patterns of learners. We hope that expected outcomes will have important implications for online learning designer, instructor and learners.

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