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Conference Proceedings National Level Virtual Conference on E-Education, E-Learning, E- Management and E-Business (NC4E) Organizing Committee of NC4E
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In spite of their effectiveness, learning environments often fail to engage users and end up under-used. Many studies show that gamification of learning environments can enhance learners' motivation to use learning environments. However, learners react differently to specific game mechanics and little is known about how to adapt gaming features to learners' profiles. In this paper, we propose a process for adapting gaming features based on a player model. This model is inspired from existing player typologies and types of gamification elements. Our approach is implemented in a learning environment with five different gaming features, and evaluated with 266 participants. The main results of this study show that, amongst the most engaged learners (i.e. learners who use the environment the longest), those with adapted gaming features spend significantly more time in the learning environment. Furthermore, learners with features that are not adapted have a higher level of amotivation. These results support the relevance of adapting gaming features to enhance learners' engagement, and provide cues on means to implement adaptation mechanisms.
In recent years, there has been an increasing interest in applying Augmented Reality (AR) to create unique educational settings. So far, however, there is a lack of review studies with focus on investigating factors such as: the uses, advantages, limitations, effectiveness, challenges and features of augmented reality in educational settings. Personalization for promoting an inclusive learning using AR is also a growing area of interest. This paper reports a systematic review of literature on augmented reality in educational settings considering the factors mentioned before. In total, 32 studies published between 2003 and 2013 in 6 indexed journals were analyzed. The main findings from this review provide the current state of the art on research in AR in education. Furthermore, the paper discusses trends and the vision towards the future and opportunities for further research in augmented reality for educational settings.
Data analytics converts bulk of data into insights for business, healthcare, insurance and education. An upcoming development in IBM’s data analytic approach towards education is cognitive learning systems. Human being and machine can communicate each other by the technologies that use Natural Language processing and Machine Language together in action. Presently, many students struggle for their education without any goals. In this sense, cognitive systems should improve student education and results with a customized perception of their learning. IBM has recently pointed his service on education by its supercomputer or computing technology, IBM Watson. Such systems can provide an expert assistant to all varies of professionals in their respective fields. It is also being used widely to assess student performance and to help educators in the classroom develop more constructive instructional practices for their students. It helps the teacher to collect attendance, marks detail and to analyse the individual student’s interest based on his result. This industrial analysis will explain the power of data analytics in classroom by the teachers to assess the student’s personal behaviour and the way it is used as a tool by the teachers to determine student’s interest in finding the better career. Based on individual student outcome, Watson using AI will find solutions to improve quality and policy of education. Here AI technology gives tools to the teachers they need to be most effective and help learners perform at the top of their abilities like tutors, childhood vocabulary development and personalizes content for students based on mastery. Data Digital services and apps are used on learning and they help in the learning experience. This study will help to understand the way different technologies working together in predictive analytics and to prepare a report on admission, number of attendances, student dropout rate, their result analysis and their future. This study will analyse the success rate of personalised education of student using cognitive learning skills.
Contribution: An improved inverted lecturing (IIL) framework based on blending flipped lectures and hands-on experiments provides instructional benefits, compared to traditional teaching (TT) and inverted lecturing (IL), in an introductory course in digital systems. Background: IL has proven more effective than TT in improving student learning in engineering courses, but has mostly been used for theory sessions. The impact of combining inverted lectures and hands-on experiments on student learning has not still been thoroughly assessed in engineering courses. Intended Outcomes: Attendance, marks, and satisfaction should improve for students in IL-based theory lectures, compared with those receiving TT, and should improve still further for students receiving the IIL-based method. Workload both for student and instructor should not increase significantly. Application Design: The three methods were compared in six consecutive offerings of the course. In the first two, TT was used for both theory and laboratory classes; in the next two, IL was used for theory lectures; and in the final two offerings the IIL-based scheme was used. The instructor, intended learning outcomes (ILOs), course syllabus, and student grading scheme were constant over the six semesters. A total of 184 students with similar backgrounds participated. Findings: Students under the IL- and IIL-based frameworks were more engaged than those receiving TT, and were more satisfied with their learning process. The IIL-based learners achieved the deepest conceptual understanding. Finally, the IL- and IIL-based methods did not significantly increase workload for either the students or the instructor.
Technology-enhanced learning has attracted increasing attention of educational community focused on improvement of traditional classroom learning. Augmented immersive reality (AIR) technologies enhance users' perception of reality by augmenting it with computer-generated components such as audio, video, 2/3-D graphics, GPS data, etc. The AIR introduces new dimensions of learning experience that ensure better attention, focus, and entertainment, thereby boosting students' motivation and attainment. This work presents an award winning AIR-based educational mobile system, code-named AIR-EDUTECH, that was developed to help high school students learn chemistry. The AIR-EDUTECH introduced new AIR features to help students better understand and learn basic concepts of molecular chemistry. It offers immersive 3D visualization and visual interaction with the examined structures that provides a broader and more retentive knowledge and improves intuition around forming basic chemical reactions. The system was introduced and tested in a field study with 45 students in the 11
th
grade chemistry class, and its impact was evaluated by the formal assessment quiz along with the feedback from survey conducted after the trial. Collected data have been subjected to an in-depth multi-modal quantitative analysis that revealed that AIR-EDUTECH stimulated significant improvements in understanding and retention of the taught content as well as turned learning chemistry into a fun, interesting and interactive experience. It also uncovered a hidden structure of taught knowledge dependencies and highlighted the role that AIR technology could play in reinforcing the retention of critical knowledge that may otherwise widen student knowledge gaps.
A distinctive feature of game-based learning environments is their capacity for enabling stealth assessment. Stealth assessment analyzes a stream of fine-grained student interaction data from a game-based learning environment to dynamically draw inferences about students' competencies through evidence-centered design. In evidence-centered design, evidence models have been traditionally designed using statistical rules authored by domain experts that are encoded using Bayesian networks. This article presents DeepStealth, a deep learning-based stealth assessment framework, that yields significant reductions in the feature engineering labor that has previously been required to create stealth assessments. DeepStealth utilizes end-to-end trainable deep neural network-based evidence models. Using this framework, evidence models are devised using a set of predictive features captured from raw, low-level interaction data to infer evidence for competencies. We investigate two deep learning-based evidence models, long short-term memory networks (LSTMs) and n-gram encoded feedforward neural networks (FFNNs). We compare these models' predictive performance for inferring students' knowledge to linear-chain conditional random fields (CRFs) and nave Bayes models. We perform feature set-level analyses of game trace logs and external pre-learning measures, and we examine the models early prediction capacity. The framework is evaluated using data collected from 182 middle school students interacting with a game-based learning environment for middle grade computational thinking. Results indicate that LSTM-based stealth assessors outperform competitive baseline approaches with respect to predictive accuracy and early prediction capacity. We find that LSTMs, FFNNs, and CRFs all benefit from combined feature sets derived from both game trace logs and external pre-learning measures.
Massive Open Online Courses (MOOCs), a unique form of online education enabled by web-based learning technologies, allow learners from anywhere in the world with any level of educational background to enjoy online education experience provided by many top universities all around the world. Traditionally, MOOC learning contents are always delivered as text-based or video-based materials. Although introducing immersive learning experience for MOOCs may sound exciting and potentially significative, there are a number of challenges given this unique setting. In this paper, we present the design and evaluation methodologies for delivering immersive learning experience to MOOC learners via multiple media. Specifically, we have applied the techniques in the production of a MOOC entitled Virtual Hong Kong: New World, Old Traditions, led by AIMtech Centre, City University of Hong Kong, which is the first MOOC (as our knowledge) that delivers immersive learning content for distant learners to appreciate and experience how the traditional culture and folklore of Hong Kong impact upon the lives of its inhabitants in the 21st Century. The methodologies applied here can be further generalised as the fundamental framework of delivering immersive learning for future MOOCs.
The Massive Open Online Course (MOOC) has expanded significantly in recent years. With the widespread of MOOC, the opportunity to study the fascinating courses for free has attracted numerous people of diverse educational backgrounds all over the world. In the big data era, a key research topic for MOOC is how to mine the needed courses in the massive course databases in cloud for each individual (course) learner accurately and rapidly as the number of courses is increasing fleetly. In this respect, the key challenge is how to realize personalized course recommendation as well as to reduce the computing and storage costs for the tremendous course data. In this paper, we propose a big data-supported, context-aware online learning-based course recommender system that could handle the dynamic and infinitely massive datasets, which recommends courses by using personalized context information and historical statistics. The context-awareness takes the personal preferences into consideration, making the recommendation suitable for people with different backgrounds. Besides, the algorithm achieves the sublinear regret performance, which means it can gradually recommend the mostly preferred and matched courses to learners. Unlike other existing algorithms, ours bounds the time complexity and space complexity linearly. In addition, our devised storage module is expanded to the distributed-connected clouds, which can handle massive course storage problems from heterogenous sources. Our experiment results verify the superiority of our algorithms when comparing with existing works in the big data setting.
The increased popularity of tablets in general has led to uptake in education. We critically review the literature reporting use of tablets by primary and secondary school children across the curriculum, with a particular emphasis on learning outcomes. The systematic review methodology was used, and our literature search resulted in 33 relevant studies meeting the inclusion criteria. A total of 23 met the minimum quality criteria and were examined in detail (16 reporting positive learning outcomes, 5 no difference and 2 negative learning outcomes). Explanations underlying these observations were analysed, and factors contributing to successful uses of tablets are discussed. While we hypothesize how tablets can viably support children in completing a variety of learning tasks (across a range of contexts and academic subjects), the fragmented nature of the current knowledge base, and the scarcity of rigorous studies, makes it difficult to draw firm conclusions. The generalizability of evidence is limited, and detailed explanations as to how, or why, using tablets within certain activities can improve learning remain elusive. We recommend that future research moves beyond exploration towards systematic and in-depth investigations building on the existing findings documented here.