Purpose The potential capabilities and benefits that learning analytics can provide are not fully utilized. A current stream of research suggests that learning analytics has more to offer for continuous improvement of higher education institutions. This study aims to explore the opportunities that data analytics stand to offer higher education and the challenges that plays down its role, adoption and usage in different areas of higher education institutions. Design/methodology/approach This study adopts a systematic literature review approach in answering the research questions. The critical role of learning analytics and the exploratory nature of research questions justify the use of systematic literature review. The current study used systematic research process adapted and presented by Hallinger (2013) to be used in social sciences in general and in educational leadership and management in particular. A standard process of finding relevant articles and examining reference lists is followed using articles from higher education which is the research context. Findings An examination of the literature showed that the majority of studies within the sample of articles are empirical representing 53 per cent, 32 per cent are conceptual, while only 15 per cent of the articles are a systematic literature review. Results also show that 58 per cent of the articles are teaching and learning related, 34 per cent are management related, while only 8 per cent are research related. Several challenges and opportunities of learning analytics in the three areas highlighted are presented and discussed. Originality/value The benefits and challenges of learning analytics are numerous and scattered in the literature. In this study, a typology related to different educational domains is developed to shed light on the benefits and challenges of learning analytics within particular higher education areas that are relevant to specific stakeholders. Benefits and challenges of learning analytics are classified into being management related, teaching and learning related and research related.

Purpose: The purpose of this paper is to determine potential identifiers of students ' academic Findings: The data-logs of ALEKS include information about number of topics practiced and number of topics mastered by each student. Prior knowledge and derived attribute, which is the ratio of number of topics mastered to number of topics practiced(denoted by the variable m top in this paper) are found to be predictors of final marks in the foundation mathematics course with= 42%.

Background The COVID-19 pandemic had a significant impact on the mental well-being of students worldwide. There is a scarcity of information on the mental health impact of the COVID-19 pandemic on university students in the United Arab Emirates (UAE). This study aimed to investigate the mental health impact of the COVID-19, including depression, anxiety and resilience among a sample of university students in the UAE. Methods A cross-sectional study using an online survey was conducted from September to November 2021. The patient health questionnaire (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Connor–Davidson Resilience Scale (CD-RISC-10) were used to assess depression, anxiety, and resilience. The COVID-19 impact was assessed using a list of questions. Results Only, 798 students completed the survey and were analyzed for this study. Overall, 74.8% of the students were females, 91.2% were never married, and 66.3% were UAE-nationals. Based on PHQ-9 and GAD-7 cut-off scores (≥ 10), four out of ten of the students self-reported moderate to severe depression (40.9%) and anxiety (39.1%). Significantly higher mean PHQ-9 and GAD-7 scores were found among students who were impacted by COVID-19 than those non-impacted (mean PHQ-9 = 9.51 ± 6.39 and 6.80 ± 6.34; p = 0.001, respectively) and (mean GAD-7 = 9.03 ± 6.00 and 8.54 ± 6.02; respectively, p < 0.001). Female students who were impacted by COVID-19 had statistically significant higher depression and anxiety scores (mean PHQ-9 of 9.14 ± 5.86 vs. 6.83 ± 6.25, respectively; p < 0.001) than the non-impacted females (mean GAD-7 of 9.57 ± 6.32 vs. 5.15 ± 3.88, respectively; p = 0.005). Never married students had significantly higher PHQ-9 and GAD-7 scores than ever-married (9.31 ± 6.37 vs. 6.93 ± 5.47, P = 0.003) and (8.89 ± 6.11 vs. 7.13 ± 5.49, respectively; p = 0.017). Conclusions The results of this study demonstrate that the COVID-19 pandemic has negatively impacted the mental health of this sample of university students in terms of depression and anxiety. The results highlight the need to adopt culturally appropriate interventions for university students and focus on vulnerable groups.

Intelligent tutoring systems are being used to teach mathematics due to the fact that they can provide individual tutoring, instant feedback on learning and flexibility to learn at own pace. In such computer supported learning environments, it is necessary that students develop the ability to learn independently. This paper reports the result obtained after mining the data logs generated by the intelligent tutoring system, ALEKS. Three clusters were generated after using two-step cluster analysis based on a variable which represents students' ability to learn independently. It was further found that students' marks in the coursework and in the final exam differed significantly among the three clusters.

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