“Clicking vs. Writing”—The Impact of a Chatbot’s Interaction Method on the Working Alliance in AI-based Coaching

Coaching has become an important didactic tool for reflecting learning processes in higher education. Digital media and AI-based technologies such as chatbots can support stimulating self-coaching processes. For the use case of student coaching on the topic of exam anxiety, the working alliance between a coaching chatbot and a human coachee is investigated. Two coachbot interaction methods are compared: A click-based chatbot (implemented in a rule-based system), where the coachee can only click on one answer, and a writing-based chatbot (implemented in a conversational AI), which allows the coachee to freely type in their answers. The focus is on which coachbot interaction method enables a stronger working alliance between coach and coachee: a click-based or a writing-based chatbot. The working alliance and the technical realization of the chatbot systems were investigated in an exploratory quantitative study with 21 engineering students. The results indicate that the working alliance in both study conditions can be classified as medium to high overall. The results further show higher values for bonding on a writing-based platform than when using a click-based system. However, click-based systems seem to be more helpful as a low-threshold entry point to coaching, as they guide coachees better through the process by providing predefined answers. An evaluation of the technical realization shows that self-reflection processes through digital self-coaching via chatbot are generally well accepted by students. For further development and research, it is therefore recommendable to develop a “mixed” coachbot that allows interaction via clicking as well as via free writing.

Study on Sentiment Classification Strategies Based on the Fuzzy Logic with Crow Search Algorithm

In recent times, sentiment analysis research has gained wide popularity. That situation is caused by the nature of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the Fuzzy rule-based system (FRBS) with crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. We compared the performance of our proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. We tested our model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrated the effectiveness of the proposed model and achieved competitive performance in terms of accuracy, recall, precision, and the F–score.

Towards Effective Patient Simulators

In this paper we give an overview of the field of patient simulators and provide qualitative and quantitative comparison of different modeling and simulation approaches. Simulators can be used to train human caregivers but also to develop and optimize algorithms for clinical decision support applications and test and validate interventions. In this paper we introduce three novel patient simulators with different levels of representational accuracy: HeartPole, a simplistic transparent rule-based system, GraphSim, a graph-based model trained on intensive care data, and Auto-ALS—an adjusted version of an educational software package used for training junior healthcare professionals. We provide a qualitative and quantitative comparison of the previously existing as well as proposed simulators.

A Fuzzy Rule-Based System for Classification of Diabetes

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions

Detecting at-risk students provides advanced benefits for reducing student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (RF). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general.

Implementation of multimarker augmented reality on solar system simulations

The solar system is one of the natural phenomena taught at school. However, delivering the material is still text-based. One of the current technology-based learning uses technology Augmented Reality as a support for learning aid. Augmented Reality is an integrated two worlds, the real and the virtual. Augmented Reality for the solar system learning application was developed by applying the concept of the Rule-Based System algorithm as a simple artificial intelligence that aims to help augmented reality systems in simulating knowledge and experience from humans with several rules prepared. The existence of Augmented Reality facilitates the process of learning on specific topics such as the solar system more attractive and interactive, with aims to inspire students to learn the solar system. Based on the testing results at SDN Purwantoro 2 Malang, Indonesia 95% of respondents are interested and captivated by learning media applications using Augmented Reality technology.

Implementation of multimarker augmented reality on solar system simulations

The solar system is one of the natural phenomena taught at school. However, delivering the material is still text-based. One of the current technology-based learning uses technology Augmented Reality as a support for learning aid. Augmented Reality is an integrated two worlds, the real and the virtual. Augmented Reality for the solar system learning application was developed by applying the concept of the Rule-Based System algorithm as a simple artificial intelligence that aims to help augmented reality systems in simulating knowledge and experience from humans with several rules prepared. The existence of Augmented Reality facilitates the process of learning on specific topics such as the solar system more attractive and interactive, with aims to inspire students to learn the solar system. Based on the testing results at SDN Purwantoro 2 Malang, Indonesia 95% of respondents are interested and captivated by learning media applications using Augmented Reality technology.