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The virtual classroom continues to grow, but it is becoming more and more the norm, and it is fundamentally different from the vocational students at the Indonesian university. With the promised benefits of the virtual classroom, many challenges and difficulties come in the implementation. Although there are already successful design principles for virtual classrooms that support organizations in overcoming the challenges, the approach to implementing the design principles of virtual classroom at the vocational higher education in Indonesia is still lacking. In this study, we aim to answer the research gap and used the design sciences research by interviewing the lecturers to design the solutions. The proposed design approaches were implemented in a course and evaluated with students from two different groups. Overall, the evaluation of the proposed approaches shows1 significant results as an indicator of the benefits of the implementation of a virtual classroom for vocational students in Indonesia.
Artificial Intelligence Driven Human-Machine Collaboration Scenarios in Virtual Reality (Poster)
(2018)
RELEVANCE & RESEARCH QUESTION: Currently the effectiveness of Virtual Reality (VR) and Augmented Reality (AR) systems as practice teaching methods are virtually uncharted. The proof that these systems can provide the same or better learning outcomes than a text instructed practical task could represent a significant benefit for educational activities. METHODS & DATA: To fathom the effectiveness, an experimental study with the three conditions (VR, AR and a real setup) were used to teach participant how to assemble a standard computer. Each condition was divided into two parts: part one in which participants were confronted with their specific scenario, part two in which participants had to go through a real practice after one week. The learning outcome was determined by the designation of hardware parts, a quiz that queried their function and the correct assembling of the components in addition to needed time. Apart from the mere performance, the acceptance of such application in academic context and difference in evaluation by men and women were of interest. RESULTS: Results concerning the Learning Outcome showed that participants from the VR condition outperformed those learned from the real setup ((M=10.0, SD=0.0) [virtual reality] vs. (M=8.95, SD=1.27) [control]). Furthermore, results from the assembling duration assessment demonstrated that VR Group Participants completed their tasks 6.62% faster than the control group. Regarding the identification of Hardware Parts, both groups scored a significant improvement during the post condition compared to the first test run, indicating a learning progress. However, due to the VR group achieving a better outcome in average answers and a more significant difference between the trials, the results indicate a better performance by participants assigned to the VR condition. ADDED VALUE: The results revealed that VR and AR systems could exceed text-based approach in terms of learning outcome performance. The effectiveness of the systems implicates a major benefit for the educational landscape, as learning content that is not realizable in terms of cost, distance or logistics could be designed as an immersive and engaging experience.
Today usually every student owns a reasonably powerful mobile device that allows to be integrated in scenarios. One of the drawbacks of the fast evolution of reasonably powerful devices, is the
heterogeneity of that these kind of devices us ually bring with them. This paper provides an overview how rich mobile learning scenarios can be implemented platform independent on the basis of HTML5 and JavaScript. The paper presents a mobile learning application based on the principles of Situated Lea
rning entirely developed in HTML5. The paper also presents the results of tests performed with the application which were aimed at finding out the difference in performance users perceived when compared with the native desktop version of the
application and the added value that mobility introduces in learning activities.
Blended learning offers learning solutions for higher educational institutions facing the industrial revolution 4.0. In this study, we investigated the influence factors student perceptions of blended learning based on gender-specific differences in Indonesia. We applied a research model to systematically assess the effect of design features on the effectiveness of blended learning indicators (intrinsic motivation and student satisfaction). Moreover, we evaluated the research model for both genders separately. Based on the quantitative survey of 223 Indonesian students, our study confirms that the design features significantly influence the effectiveness of blended learning for male and female students.
Applications and research efforts in Mobile Learning constitute a growing field in the area of Technology Enhanced Learning. However, despite a permanent increase of mobile internet accessibility and availability of mobile devices over the past years, a mobile learning environment that is easy to use, widely accepted by teachers and learners, uses widespread off-the-shelf software, and that covers various application scenarios and mobile devices, is not yet available. In this paper, we address this issue by presenting an approach and technical framework called "Mobile Contributions" ("MoCo"). MoCo supports learners to create and send contributions through various channels (including third-party solutions like Twitter, SMS and Facebook), which are collected and stored in a central repository for processing, filtering and visualization on a shared display. A set of different learning and teaching scenarios that can be realized with MoCo are described along with first experiences and insights gained from qualitative and quantitative evaluation.
This paper presents an approach towards a mobile learning environment, which is flexible in terms of supported scenarios, supported devices and input channels. The approach makes use of existing and commonly used channels like SMS, Twitter or Face book to increase acceptance and ease-of-use of mobile devices in learning scenarios. Envisaged application scenarios are described along with technical details for their realization.
Developing an intelligent chatbot that can imitate human-to-human interaction has become important in recent years. For this reason, many studies have been conducted to evaluate the quality of chatbots. Furthermore, various approaches and tools, such as sentiment analysis, have been created to improve the performance of chatbots.
This study examines previous research to identify the quality dimensions used to measure chatbots performance in order to develop a general chatbot assessment model that evaluates and compares chatbots quality. The developed evaluation model measures ten chatbot quality dimensions. This model is based on user experience. It requires human testers to interact with the chatbot to test its functioning and then a quantitative approach is used to collect data from user testing by conducting a survey with these testers. In this survey, they are instructed to evaluate the quality of the chatbot using a questionnaire that contains the items needed to evaluate each dimension.
This study also investigates whether sentiment analysis can improve the quality of chatbots and, if so, to identify the dimensions improved with sentiment analysis. For this reason, two chatbot versions are implemented using the Rasa framework (one that cannot detect sentiment and the other that analyzes sentiment and responds accordingly).
Following that, we used our evaluation model to evaluate and compare the two chatbot versions with two groups of participants by conducting a survey. In this survey, each group tested the functioning of one version. Then, both groups were instructed to use the items of the evaluation model to evaluate the version they tested. The goal of this survey was to evaluate the validity and reliability of the items used in the evaluation model to evaluate chatbots, and also to determine if sentiment analysis improved the chatbot quality by comparing survey results between the two groups. The results show that items used in the assessment model to evaluate chatbots are valid and reliable. The findings also indicate that sentiment analysis improves the chatbot’s quality. However, it improves the quality of some dimensions but not the majority of them.