The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education.
Augmented Reality (AR) is a technology that enhances a person’s sensory perception by overlaying virtual objects in the user’s immediate surroundings. Furthermore, with the development of technologies, devices such as smartphones and head-mounted displays are being launched and are expanding the AR technology application sectors from research labs to a wide range of domains. On the other hand, Geospatial Information System (GIS) is capable of dealing with geospatial information, so it can be beneficial in most AR systems mainly because those systems are connected to location and information related to locations. The ultimate integrated solution could be beneficial for Sustainable Development Goals (SDGs). This paper investigates the combination of AR and GIS. Specifically, it studies the advantages of integration to address the challenges available in systems employing merely one of the technologies. The presented findings would assist researchers in future studies on utilizing GIS and AR simultaneously by giving an overview of the current applications and challenges.
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