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A fixed learning path for all learners is a major drawback of virtual learning systems. An online learning path recommendation system has the advantage of offering flexibility to select appropriate learning content. Learning Analytics Intervention (LAI) provides several educational benefits, particularly for low-performing students. Researchers employed an LAI approach in this work to recommend personalised learning paths to students pursuing online courses depending on their learning styles. It was accomplished by developing a Learning Path Recommendation Model (LPRM) based on the Felder–Silverman Learning Style Model (FSLSM) and evaluating its efficacy. The data were analysed with the help of a dataset from the Moodle Research Repository, and different learning paths were recommended using a sequence matching algorithm. The effectiveness of this approach was tested in two groups of learners using the independent two-sample t-test, a statistical testing tool. The experimental evaluation revealed that learners who followed the suggested learning path performed better than those who followed the learning path without any recommendations. This enhanced learning performance exemplifies the effects of learning analytics intervention .

The prevalence of mobile devices led the authorities to collect enormous volume of spatio-temporal trajectories. This practice may lead to the exposition of valuable sensitive personal details to adversaries and thereby privacy may be endangered. But the publication of datasets is essential for developmental activities. Hence anonymization of trajectory before publishing is imperative. In this work, instead of anonymizing the whole trajectory, it focuses on some stay locations on the trajectory as most sensitive to the user and anonymized them to provide privacy. This work suggests a perfect blend of existing generalization techniques with Places of Interests along with a new temporal perturbation technique for the anonymization of sensitive stay locations by adding temporal noise values. The experiments and evaluations with real-world datasets prove that this approach reduces unnecessary anonymization of trajectories and provide high data utility, less information loss and greater privacy for the users during the trajectory publication.

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