As an emerging field of research, learning analytics (LA) offers practitioners and researchers information about educational data that is helpful for supporting decisions in management of teaching and learning. While often combined with educational data mining (EDM), crucial distinctions exist for LA that mandate a separate review. This study aims to conduct a systematic meta-review of LA for mining key information that could assist in describing new and helpful directions to this field of inquiry. Within 901 LA articles analyzed, eight reviews were identified and synthesised to identify and determine consistencies and gaps. Results show that LA is at the stage of early majority and has attracted great research efforts from other fields. The majority of LA publications were focused on proposing LA concepts or frameworks and conducting proof-of-concept analysis rather than conducting actual data analysis. Collecting small datasets for LA research is predominant, especially in K-12 field. Finally, four major LA research topics, including prediction of performance, decision support for teachers and learners, detection of behavioural patterns & learner modelling and dropout prediction, were identified and discussed deeply. The future research of LA is also outlined for purpose of better understanding and optimising learning as well as learning contexts.

Performance prediction is a leading topic in Learning Analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction rather than identifying key performance factors; the lack of common predictors identified for both K-12 and higher education environments; and the misplaced focus on absolute engagement levels rather than relative engagement levels. Two datasets, one from higher education and the other from a K-12 online school with 13,368 students in more than 300 courses, were applied using the predictive modeling technique. The results show the newly suggested approach had higher overall accuracy and sensitivity rates than the traditional approach. In addition, two generalizable predictors were identified from instruction-intensive and discussion-intensive courses.

Despite years of primary research on problem-based learning and literature reviews, no systematic effort has been made to analyze the relationship between tutor characteristics and student learning outcomes. In an effort to fill that gap the following meta-analysis coded 223 outcomes from 94 studies with small but positive gains for PBL students (g = 0.24). Specific sub-group analyses indicate randomly controlled designs may be more sensitive to differences that favor PBL students, even while there is no relationship between tutor content expertise and student learning. Perhaps surprisingly, student learning decreases as tutor experience increases. Limitations and future work are discussed within a context of scholarly and practical significance.

The study proposes two new measures, time and location entropy, to depict students’ physical spatio-temporal contexts when engaged in an online course. As anytime, anywhere access has been touted as one of the most attractive features of online learning, the question remains as to the success of students when engaging in online courses through disparate locations and points-in-time. The procedures describe an analysis of 5293 students’ spatio-temporal patterns using metadata relating to place and time of access. Grouping into segments that describe their patterns of engagement, results indicate that the high location-high time entropy (i.e. multiple times, multiple locations) was the segment with lowest success when compared with other students. Statistical and modeling results also found that female students tended to learn at fixed or few locations resulting in the highest performance scores on the final exam. The primary implication is that female students tend to be successful because they study in fewer locations, and all students who study at consistent times outperform those with more varied time patterns. Existing brain research supports the findings on gender differences in learning performance and spatio-temporal characteristics.

Utilizing Geo-referenced Mobile Game Technology for Universally Accessible Virtual Geology Field Trips

Technologies that provide immersive experiences continue to become more ubiquitous across all age groups. This paper presents a review of the literature to provide a snapshot of the current state of research involving the use of immersive technologies and the elderly. A narrative literature review was conducted using the ScienceDirect, EBSCOhost, Springerlink and ERIC databases to summarize primary studies from which conclusions were drawn into a holistic interpretation. The majority of the studies examined the effect of immersive technologies on elder peoples' age-related declines, including sensory and motor changes (vision, hearing, motor skills), cognitive changes and social changes. Various immersive technologies have been described and tested to address these age-related changes, and have been categorized as ‘games and simulations', ‘robotics' and ‘social technologies'. In most cases, promising results were found for immersive technologies to challenge age-related declines, especially through the increase of morale.