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Technologies and teaching practices can provide a rich log data, which enables learning analytics (LA) to bring new insights into the learning process for ultimately enhancing student success. This type of data has been used to discover student online learning patterns, relationships between online learning behaviors and assessment performance. Previous studies have provided empirical evidence that not all log variables were significantly associated with student academic achievement and the relationships varied across courses. Therefore, this study employs a systematic review with meta‐analysis method to provide a comprehensive review of the log variables that have an impact on student academic achievement. We searched six databases and reviewed 88 relevant empirical studies published from 2010 to 2021 for an in‐depth analysis. The results show different types of log variables and the learning contexts investigated in the reviewed studies. We also included four moderating factors to do moderator analyses. A further significance test was performed to test the difference of effect size among different types of log variables. Limitations and future research expectations are provided subsequently. Practitioner notesWhat is already known about this topic Significant relationship between active engagement in online courses and academic achievement was identified in a number of previous studies.Researchers have reviewed the literature to examine different aspects of applying LA to gain insights for monitoring student learning in digital environments (eg, data sources, data analysis techniques).What this paper adds Presents a new perspective of the log variables, which provides a reliable quantitative conclusion of log variables in predicting student academic achievement.Conducted subgroup analysis, examined four potential moderating variables and identified their moderating effect on several log variables such as regularity of study interval, number of online sessions, time‐on‐task, starting late and late submission.Compared the effect of generic and course‐specific, basic and elaborated log variables, and found significant difference between the basic and elaborated.Implications for practice and/or policy A depth of understanding of these log variables may enable researchers to build robust prediction models.It can guide the instructors to timely adjust teaching strategies according to their online learning behaviors. [ABSTRACT FROM AUTHOR]

Gamification is typically implemented digitally. However, digital gamification is not always possible because of limited resources and logistical problems. It is thus necessary to explore low‐threshold gamification methods that can be easily adopted in classrooms. One such method is non‐digital gamification using physical game design elements. Currently, the literature provides a limited understanding of the design and effects of non‐digital gamification. It is also unclear whether mixed gamification (ie, a combination of digital and non‐digital gamification) is better than purely non‐digital gamification. We explored these topics using an explanatory sequential mixed‐methods design to compare the effectiveness of non‐digital gamification in a face‐to‐face course and mixed gamification in a blended course in terms of student learning achievement, cognitive engagement and course satisfaction. Two secondary grade classes were randomly assigned to the mixed (N = 52) or non‐digital (N = 52) gamification conditions. Contrary to our expectations, the quantitative results showed that the two approaches improved the students' learning achievement to the same extent. Although the students in the non‐digital gamification condition reported significantly higher‐course satisfaction than those in the mixed condition, the improvement in student cognitive engagement was significantly greater in the mixed gamification condition than in the non‐digital condition. Through focus group interviews, we analysed the students' learning experiences and synthesised the factors that influenced student cognitive engagement and course satisfaction. Practitioner notesWhat is already known about this topic Gamification is increasingly being used to increase student engagement and enhance learning.Gamification studies have mostly focused on digital modes, which often require gamified digital platforms or applications.Non‐digital gamification can be an effective method to foster learning, but the design and evaluation of non‐digital gamification is at an early stage.What this paper adds We designed non‐digital and mixed (ie, a combination of digital and non‐digital) gamification programmes and compared their effects using an explanatory sequential mixed‐methods design.Both gamification approaches led to a similar improvement in learning achievement.While non‐digital gamification fostered students' course satisfaction, mixed gamification was more effective in improving students' cognitive engagement.Implications for practice and/or policy Non‐digital gamification can be an effective low‐threshold solution for instructors. It is worth further improving the non‐digital gamification approach in school settings as it is cost‐effective and can be transferred between classrooms.The benefits of mixed gamification should not be ignored because of its affordances that improve students' cognitive engagement. [ABSTRACT FROM AUTHOR]

This study presents an advanced predictive learning analytics system, OU Analyse (OUA), and evidence from its evaluation with online teachers at a distance learning university. OUA is a predictive system that uses machine learning methods for the early identification of students at risk of not submitting (or failing) their next assignment. Teachers have access, via interactive dashboards, to weekly predictions of risk of failing for each of their students. In this study, we examined how the degree of OUA usage by 559 teachers, of which 189 were given access to OUA, related to student learning outcomes of more than 14 000 students in 15 undergraduate courses. Teachers who made "average" use of OUA, that is accessed OUA throughout the life cycle of a course presentation, and in particular between 10% and 40% of the weeks a course was running, and intervened with students flagged as at risk were found to benefit their students the most; after controlling for differences in academic performance, these students were found to have significantly better performance than their peers in the previous year's course presentation during which the same teachers made no use of predictive learning analytics. Predictive learning analytics is an innovative student's support approach in online pedagogy that, as shown in this study, can empower online teachers in effectively monitoring and intervening with their students, over and above other approaches, and result in improved learning outcomes. Practitioner NotesWhat is already known about this topic Pedagogical and personal support to students is a significant responsibility of online teachers.Student's support is a challenging activity due to the lack of face‐to‐face interactions.Predictive learning analytics (PLA) can identify students at risk of failing their studies.What this paper adds One of the few large‐scale studies is available for examining the impact of analytics on student's performance.Teachers' usage of PLA was significantly related to better learning outcomes.Online teachers had students with better learning outcomes when accessing PLA data rather than when they had no access.Implications for practice and/or policy PLA can empower online teachers and complement the teaching practice.PLA can help in the identification and proactive intervention of students at risk of failing their studies.Actions should be taken to motivate and engage online teachers with PLA. [ABSTRACT FROM AUTHOR]

Compared with fully face‐to‐face or online learning environments, implementation of hybrid learning spaces is costly given the spaces making all learning options available for learners. Therefore, decisions on investments in hybrid learning are critical for institutions. Satisfaction and experience of learners is one of the important indicators for assessing the cost‐effectiveness of learning space implementation; thus, predictions of learners' satisfaction and experience can inform institutions' decision making on learning space investments. Moreover, learning competences are found correlated with learners' satisfaction and experience in general and e‐learning settings. Therefore, the present study aimed at exploring predictive learning competences for hybrid learners' experience and satisfaction. A hybrid learning space was built upon a proposed model at Shanghai Open University. 211 students' learning competences and their satisfaction and experience in the hybrid learning space were examined. The results showed that except cognitive engagement competence, most predictive competences were not significantly associated with hybrid learners' satisfaction and experience. The findings indicated that since hybrid learning keeps all options available, to experience satisfying learning, students need not have certain competences but cognitive engagement competence, which is correlated with learners' cognitive ability to figure out the right mix of learning options. [ABSTRACT FROM AUTHOR]