Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing learning difficulties. While LADs have gained popularity, existing LADs are still far behind when it comes to employing predictive analytics into their designs. Our systematic literature review has revealed limitations in the utilization of predictive analytics tools among existing LADs. We find that studies leveraging predictive analytics only go as far as identifying the at-risk students and do not employ model interpretation or explainability capabilities. This limits the ability of LADs to offer data-driven prescriptive advice to students that can offer them guidance on appropriate learning adjustments. Further, published studies have mostly described LADs that are still at prototype stages; hence, robust evaluations of how LADs affect student outcomes have not yet been conducted. The evaluations until now are limited to LAD functionalities and usability rather than their effectiveness as a pedagogical treatment. We conclude by making recommendations for the design of advanced dashboards that more fully take advantage of machine learning technologies, while using suitable visualizations to project only relevant information. Finally, we stress the importance of developing dashboards that are ultimately evaluated for their effectiveness.

Learning Analytics Dashboards (LADs) are gaining popularity as a platform for providing students with insights into their learning behaviour patterns in online environments. Existing LAD studies are mainly centred on displaying students’ online behaviours with simplistic descriptive insights. Only a few studies have integrated predictive components, while none possess the ability to explain how the predictive models work and how they have arrived at specific conclusions for a given student. A further gap exists within existing LADs with respect to prescriptive analytics that generate data-driven feedback to students on how to adjust their learning behaviour. The LAD in this study attempts to address this gap and integrates a full spectrum of current analytics technologies for sense-making while anchoring them within theoretical educational frameworks. This study’s LAD (SensEnablr) was evaluated for its effectiveness in impacting learning in a student cohort at a tertiary institution. Our findings demonstrate that student engagement with learning technologies and course resources increased significantly immediately following interactions with the dashboard. Meanwhile, results showed that the dashboard boosted the respondents’ learning motivation levels and that the novel analytics insights drawn from predictive and prescriptive analytics were beneficial to their learning. This study, therefore, has implications for future research when investigating student outcomes and optimizing student learning using LAD technologies.

Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for supporting these students in overcoming their learning difficulties. However, the majority of studies have taken the approach of developing individual models that target a single course while developing prediction models. These models are tailored to specific attributes of each course amongst a very diverse set of possibilities. While this approach can yield accurate models in some instances, this strategy is associated with limitations. In many cases, overfitting can take place when course data is small or when new courses are devised. Additionally, maintaining a large suite of models per course is a significant overhead. This issue can be tackled by developing a generic and course-agnostic predictive model that captures more abstract patterns and is able to operate across all courses, irrespective of their differences. This study demonstrates how a generic predictive model can be developed that identifies at-risk students across a wide variety of courses. Experiments were conducted using a range of algorithms, with the generic model producing an effective accuracy. The findings showed that the CatBoost algorithm performed the best on our dataset across the F-measure, ROC (receiver operating characteristic) curve and AUC scores; therefore, it is an excellent candidate algorithm for providing solutions on this domain given its capabilities to seamlessly handle categorical and missing data, which is frequently a feature in educational datasets.

The National TB Elimination Programme (NTEP) of India is implementing tuberculosis preventive treatment (TPT) for all household contacts (HHCs) of pulmonary tuberculosis patients (index patients) aged 5 years with TB infection (TBI). We conducted an explanatory mixed-methods study among index patients registered in the Kolar district, Karnataka during April-December 2022, to assess the TPT cascade and explore the early implementation challenges for TPT provision. Of the 301 index patients, contact tracing home visits were made in 247 (82.1%) instances; a major challenge was index patients’ resistance to home visits fearing stigma, especially among those receiving care from the private sector. Of the 838 HHCs, 765 (91.3%) were screened for TB; the challenges included a lack of clarity on HHC definition and the non-availability of HHCs during house visits. Only 400 (57.8%) of the 692 eligible HHCs underwent an IGRA test for TBI; the challenges included a shortage of IGRA testing logistics and the perceived low risk among HHCs. As HHCs were unaware of their IGRA results, a number of HHCs actually eligible for TPT could not be determined. Among the 83 HHCs advised of the TPT, 81 (98%) initiated treatment, of whom 63 (77%) completed treatment. Though TPT initiation and completion rates are appreciable, the NTEP needs to urgently address the challenges in contact identification and IGRA testing.

Learning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on what-if modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the what-if modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on what-if counterfactuals can be used to automate student feedback through prescriptive analytics.

To improve tuberculosis (TB) care among individuals attending a private tertiary care hospital in Vietnam, an innovative private sector engagement model was implemented from June to December 2018. This included: (i) Active facility-based screening of all adults for TB symptoms (and chest x-ray (CXR) for those with symptoms) by trained and incentivized providers, with on-site diagnostic testing or transport of sputum samples, (ii) a mobile application to reduce dropout in the care cascade and (iii) enhanced follow-up care by community health workers. We conducted a cohort study using project and routine surveillance data for evaluation. Among 52,078 attendees, 368 (0.7%) had symptoms suggestive of TB and abnormalities on CXR. Among them, 299 (81%) were tested and 103 (34.4%) were diagnosed with TB. In addition, 195 individuals with normal CXR were indicated for TB testing by attending clinicians, of whom, seven were diagnosed with TB. Of the 110 TB patients diagnosed, 104 (95%) were initiated on treatment and 97 (93%) had a successful treatment outcome. Given the success of this model, the National TB Programme is considering to scale it up nationwide after undertaking a detailed cost-effectiveness analysis.