Learning management systems (LMS) are typically used by large educational institutions and focus on supporting instructors in managing and administrating online courses. However, such LMS typically use a "one size fits all" approach without considering individual learner's profile. A learner's profile can, for example, consists of his/her learning styles, goals, prior knowledge, abilities, and interests. Generally, LMSs do not cater individual learners' needs based on their profile. However, considering learners' profiles can help in enhancing the learning experiences and performance of learners within the course. To support personalization in LMS, recommender systems can be used to recommend appropriate learning objects to learners to increase their learning. In this paper, we introduce the personalized learning object recommender system. The proposed system supports learners by providing them recommendations about which learning objects within the course are more useful for them, considering the learning object they are visiting as well as the learning objects visited by other learners with similar profiles. This kind of personalization can help in improving the overall quality of learning by providing recommenda-B Hazra Imran tions of learning objects that are useful but were overlooked or intentionally skipped by learners. Such recommendations can increase learners' performance and satisfaction during the course.

Gamification has been gaining increasing acceptability in recent times in educational and commercially related activities, as a tool that encourages and improves the motivation of digital native learners. Since learners can easily engage, educationists have explored gamification as a tool for remediation of engagement, motivation, and collaboration. However, the literature showed that the structural and contextual deployment of game elements is defined only partially in practice. Subsequently, gamification success and failure factors should be explored to identify the required enhancement to achieve improved efficiency in current systems. This research extracts the relevant aspects of gamification that need due consideration to make a guided choice through existing theories. This study is based on an online gamified study that uses well-founded concepts in teaching and evaluation of students in a university. Although badges earned and time spent indicated an increase in engagement, the results show that further work needs to be done by incorporating feedback elements, social interaction, and interactive guidance. The underlying impression is that timely, frequent feedback and personalized guidance, avenues for collaboration and interactivity need to be explored towards the better utility of gamification. Therefore, learning culture in the current learner-centered environment should be further studied to infuse better productivity.

Remote sensing technology has penetrated all the natural resource segments as it provides precise information in an image mode. Remote sensing satellites are currently the fastest-growing source of geographic area information. With the continuous change in the earth’s surface and the wide application of remote sensing, change detection is very useful for monitoring environmental and human needs. So, it is necessary to develop automatic change detection techniques to improve the quality and reduce the time required by manual image analysis. This work focuses on the improvement of the classification accuracy of the machine learning techniques by reviewing the training samples and comparing the post-classification comparison with the image differencing in the algebraic technique. Landsat data are medium spatial resolution data; that is why pixel-wise computation has been applied. Two change detection techniques have been studied by applying a decision tree algorithm using a separability matrix and image differencing. The first change detection, e.g., the separability matrix, is a post-classification comparison in which individual images are classified by a decision tree algorithm. The second change detection is, e.g., the image differencing change detection technique in which changed and unchanged pixels are determined by applying the corner method to calculate the threshold on the changing image. The performance of the machine learning algorithm has been validated by 10-fold cross-validation. The experimental results show that the change detection using the post-classification method produced better results when compared to the image differencing of the algebraic change detection technique.

Abstract. Personalization in learning management systems (LMS) occurs when such systems tailor the learning experience of learners such that it fits to their profiles, which helps in increasing their performance within the course and the quality of learning. A learner's profile can, for example, consist of his/her learning styles, goals, existing knowledge, ability and interests. Generally, traditional LMSs do not take into account the learners' profile and present the course content in a static way to every learner. To support personalization in LMS, recommender systems can be used to recommend appropriate learning objects to learners, not only based on their individual profile but also based on what worked well for learners with a similar profile. In this paper, we propose a framework to integrate a recommender system approach into LMS. The proposed framework is designed with the goal of presenting a flexible integration model which can provide personalization by automatically suggesting learning objects to learners based on their current situation as well as successful learning experiences of learners with similar profiles in a similar situation. Such advanced personalization can help learners in many ways such as reducing the learning time without negative impact on their marks, improving learning performance as well as increasing the level of satisfaction.

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