How do online learners study? The psychometrics of students’ clicking patterns in online courses

Abstract

College students’ study strategies were explored by tracking the ways they navigated the websites of two large (Ns of 1384 and 671) online introductory psychology courses. Students’ study patterns were measured analyzing the ways they clicked outside of the regularly scheduled class on study materials within the online Learning Management System. Three main effects emerged: studying course content materials (as opposed to course logistics materials) outside of class and higher grades are consistently correlated; studying at any time except in the late night/early morning hours was strongly correlated with grades; students with higher Scholastic Aptitude Test (SAT) scores made higher grades but accessed course materials at lower rates that those with lower SATs. Multiple regressions predicting grades using just SATs and click rates accounted for almost 43 and 36 percent of the grade variance for the Fall and Spring classes respectively. Implications for using click patterns to understand and shape student learning are discussed.

Predictor Analysis of Middle and Low Achieving Students in Cell Biology Online Course in Health Nutrition Undergraduate Program

“…The questions in the pretest encompass all of the pre and post midterm topics, but the correlation of the pretest score with the midterm and nal exam score is low. Entrance exam scores are additional pre-course data that have been shown to be a strong predictor of students' achievement [9]. However, not every course coordinator has access to the entrance scores and they may not be equally related to student achievement in various speci c courses.…”

“…The learning strategy is another factor that can in uence students' achievement [12]. Recent research showed online learning behavior [9] and social networking in online collaborative learning [25] also contribute to student performance.…”

The Personality Panorama: Conceptualizing Personality through Big Behavioural Data

“…The shifting landscape of human experience has made the subtleties of behavioural traces particularly important: the physical and digital footprints that human behaviour leaves behind are a veritable goldmine of personality data (Lambiotte & Kosinski, 2014). The images that a person chooses to share with others (Burdick, Mihalcea, Boyd, & Pennebaker, 2020; Settanni, Azucar, & Marengo, 2018), a person's words and other features of their verbal behaviour (Boyd et al, 2015; Golbeck, 2016; Hoover, Dehghani, Johnson, Iliev, & Graham, 2018; Kern et al, 2014; Mitra, Counts, & Pennebaker, 2016; Park et al, 2015), URL clicks (Lien, Bai, & Chen, 2019; Tellakat, Boyd, & Pennebaker, 2019), social behaviours (Adali & Golbeck, 2012; Hilbig, Thielmann, Hepp, Klein, & Zettler, 2015), and self‐presentation behaviours (Liu, Preotiuc‐Pietro, Samani, Moghaddam, & Ungar, 2016; Segalin et al, 2017; Shiramizu, Kozma, DeBruine, & Jones, 2019; Todorov et al, 2005; Walker, Schönborn, Greifeneder, & Vetter, 2018) are no longer simply reflections of personality—they are the critical raw material for understanding personality itself.…”

“…Second, because trait scores can be predicted by patterns of online social behaviour (Bachrach, Kosinski, Graepel, Kohli, & Stillwell, 2012; Youyou, Kosinski, & Stillwell, 2015), and because there is value when different data sets are joined (intellectual capital, new knowledge), measures of personality will be increasingly linked with other forms of data (Oboler, Welsh, & Cruz, 2012). Third, many large–scale digital data sets will have been collected by third parties with limited ethical oversight (Thompson & Warzel, 2019), and personality scholars will frequently have little control over how the data have been collected or maintained. Finally, because personality data are and will continue to be valuable to such varied stakeholders as financial institutions, product vendors, healthcare providers, intelligence organizations, potential landlords, and prospective romantic partners, there will be a thriving market for personality–relevant data (McMullan, 2015; Wu, 2019).…”

Pengalaman Pertama Belajar dengan Online Learning

“…Blended learning didefinisikan sebagai "designates the range of possibilities presented by combining Internet and digital media with established classroom forms that required the physical copresence or teacher and student" (Friesen, 2012). Blending Learning diharapkan memberikan hasil belajar yang lebih baik (Nguyen, 2017;Lu et al, 2018) sehingga semakin banyak perguruan tinggi yang menawarkan perkuliahan dengan format ini (Ku & Lohr, 2000) (Tellakat et al, 2019). Blending Learning mengubah strategi penyampaian pembelajaran (instructional delivery strategy) sehingga mengubah bentuk interaksi dalam pembelajaran yang selanjutnya memberikan dampak kepada variabel pembelajaran lainnya.…”