Creating a learning environment in which students learn more effectively remains the great challenge from decades; different approaches are proposed, for example, Intelligent Tutoring Systems, Question Answering System and chatbot. All these approaches used natural language to achieve that goal. A comparison of these systems viz-a-viz student learning outcome and behavior is of eminent importance. To achieve this goal a chatbot system with knowledge base for Object-Oriented Programming Languages is developed and deployed. Case study was made to assess and evaluate the chatbot system for student learning methodology. Learning outcomes and Memory retention have been measured for the developed system. Comparisons were made between the results obtained using Google search engine and our chatbot system. The results indicate that learning through Chabot have a significant impact on memory retention and Learning outcomes of the students.
Serious Games (SG) provide a comfortable learning environment and are productive for various disciplines ranging from Science, Technology, Engineering, and Mathematics (STEM) to computer programming. The Object Oriented (OO) paradigm includes objects related to real life, and is considered a natural domain that can be worked with. Nonetheless, mapping those real-life objects with basic Object-Oriented Programming (OOP) concepts becomes a challenge for students to understand. Therefore, this study is concerned with designing and developing an SG prototype to overcome students’ difficulties and misconceptions in learning OOP and achieving positive learning outcomes. An experimental evaluation was carried out to show the difference between the experimental group students’ performance, who interact with the developed game, and students of the control group, who learn via the traditional instructional method. The experimental evaluations’ main finding is that the experimental group’s performance is better than the control group. The experimental group’s Normalized Learning Gain (NLG) is significantly higher than the control group (p< 0.005, paired t-test). The evaluation study results show that the developed prototype’s perceived motivation on the Instructional Materials Motivation Survey (IMMS) 5-point Likert scale resulted in the highest mean score for attention (3.87) followed by relevance (3.66) subcategories. The results of this study show that the developed SG prototype is an effective tool in education, which improves learning outcomes and it has the potential to motivate students to learn OOP.
We live in a digitally connected world inspired by state-of-the-art ICT technologies and networks, inasmuch as the use of digital gadgets and apps is exponentially increasing in all domains of life. In parallel, artificial intelligence has evolved as an essential tool in all sorts of applications and systems such as healthcare systems. Healthcare is the key domain where the use of ICT infrastructure, technologies and artificial intelligence are playing a major role in providing connected and personalized digital health experiences. The vision is to provide intelligent and customized digital health solutions and involve the masses in personal health monitoring. This research proposes AiDHealth as an intelligent personal health monitoring framework based on artificial intelligence for healthcare data analytics and connectivity for personal health monitoring. AiDHealth relies on various machine learning and deep learning models for achieving prediction accuracy in healthcare data analytics. The extensive Pima Indian Diabetes (PID) dataset has been used for investigation. The findings of our experiments illustrate the effectiveness and suitability of the suggested MLPD model. AdaBoost classifier performance has the highest accuracy in prediction when calculated to the individual classifiers. The AdaBoost classifier produced the best accuracy i.e., 0.975%. The results reveal improvements to state-of-the-art procedures in the proposed model. Next, we trained the models and produced a 10-fold cross-validation illness risk index for each sample. Our findings suggest a need for greater experiments to compare the above-mentioned machine learning methods. We identified the AdaBoost classifier and Decision Tree classifiers with the best prediction with an average of 0.975% and a work Curve Area (AUC) of 0.994%. Thus, because the design of the AdaBoost classifier is superior, it can forecast the danger of type 2 diabetes more accurately than the existing algorithms and lead to intelligent prevention and control of diabetes.
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