Augmented reality (AR) technology is mature for creating learning experiences for K-12 (pre-school, grade school, and high school) educational settings. We reviewed the applications intended to complement traditional curriculum materials for K-12. We found 87 research articles on augmented reality learning experiences (ARLEs) in the IEEE Xplore Digital Library and other learning technology publications. Forty-three of these articles conducted user studies, and seven allowed the computation of an effect size to the performance of students in a test. In our meta-analysis, research show that ARLEs achieved a widely variable effect on student performance from a small negative effect to a large effect, with a mean effect size of 0.56 or moderate effect. To complement this finding, we performed a qualitative analysis on the design aspects for ARLEs: display hardware, software libraries, content authoring solutions, and evaluation techniques. We explain that AR incur three inherent advantages: real world annotation, contextual visualization, and vision-haptic visualization. We illustrate these advantages through the exemplifying prototypes, and ground these advantages to multimedia learning theory, experiential learning theory, and animate vision theory. Insights from this review are aimed to inform the design of future ARLEs.

Augmented Reality Learning Experiences: Survey of Prototype Design and Evaluation

The challenge of the semantic web is the provision of distributed information with well-defined meaning, understandable for different parties. Particularly, applications should be able to provide individually optimized access to information by taking the individual needs and requirements of the users into account. In this paper we propose a framework for personalized e-Learning in the semantic web and show how the semantic web resource description formats can be utilized for automatic generation of hypertext structures from distributed metadata. Ontologies and metadata for three types of resources (domain, user, and observation) are investigated. We investigate a logic-based approach to educational hypermedia using TRIPLE, a rule and query language for the semantic web.

/pdf/reasoning-and-ontologies-for-personalized-e-learning-in-the-27ucexu7pf.pdf

Reasoning and Ontologies for Personalized E-Learning in the Semantic Web.

The aim of this paper is to delineate a coherent framework for the authoring of re-purposable learning objects. The approach is orthogonal to the considerable work into learning object metadata and packaging conducted by bodies such as IMS, ADL and the IEEE. The 'learning objects' and standardisation work has been driven largely by adding packaging and metadata to pre-constructed learning artefacts. This work is very valuable. The argument of this paper, however, is that these developments must be supplemented by significant changes in the creation of learning objects. The principal aim of this paper is to delineate authoring principles for reuse and repurposing. The principles are based on a synthesis of ideas from pedagogy and software engineering. These principles are outlined and illustrated from a case study in the area of learning to program in Java.

Design principles for authoring dynamic, reusable learning objects.

For future network scenarios to exhibit autonomic behaviour, both networks and application components and services need to be aware of their computational and environmental context, and must tune their activities accordingly. In this position paper, we propose an abstract architecture for knowledge networks that addresses the key issues of how both physical contextual knowledge and social knowledge from the users of communication networks can be used to form a knowledge space in support of autonomic agents dealing with network elements and applications. We discuss that the availability of raw contextual data is not enough to achieve meaningful autonomic behaviours. Rather, contextual information should be properly organised into ‘networks of knowledge', to be exploited by both network and application components as the basic ‘nervous system' in which situational stimuli reify into digital knowledge, and by means of which components can properly orchestrate their activities in a globally meaningful way. Here we firstly discuss the fundamental role of knowledge networks, and try to sketch what actual form and position such knowledge networks could assume. Then, we analyse some simple scenarios of use, showing how it is possible for the components of an autonomic communication system to build such knowledge networks autonomously; and, at the same time, to exploit them for orchestrating their activities in a type of stigmergy-based knowledge-rich system. Eventually, we sketch a rough research agenda and discuss the relations with other research areas.

Knowledge networks

Learning objects offer increased ability to share learning resources so that system-wide production costs can be reduced. But how can users select from a set of similar learning objects in a repository and be assured of quality? This article reviews recent developments in the establishment of learning object repositories and metadata standards, and presents a formative reliability analysis of an online, collaborative method for evaluating quality of learning objects. The method uses a 10-item Learning Object Review Instrument (LORI) within a Convergent Participation evaluation model that brings together instructional designers, media developers, and instructors. The inter-rater reliability analysis of 12 raters evaluating eight learning objects identified specific items in LORI that require further development. Overall, the collaborative process substantially increased the reliability and validity of aggregate learning object ratings. The study concludes with specific recommendations including changes to LORI items, a rater training process, and requirements for selecting an evaluation team.

/pdf/learning-object-evaluation-computer-mediated-collaboration-27pw1aw7pd.pdf

Learning object evaluation: Computer-mediated collaboration and inter-rater reliability

Permission is hereby granted for IEEE Standards Committee participants to reproduce this document for purposes of IEEE standardization activities only. Prior to submitting this document to another standard development organization for standardization activities, permission must first be obtained from the Manager, Standards Licensing and Contracts, IEEE Standards Activities Department. Other entities seeking permission to reproduce portions of this document must obtain the appropriate license from the Manager, Standards Licensing and Contracts, IEEE Standards Activities Department. The IEEE is the sole entity that may authorize the use of IEEE owned trademarks, certification marks, or other designations that may indicate compliance with the materials contained herein.

/pdf/draft-standard-for-learning-object-metadata-1ws5algjjl.pdf

Draft Standard for Learning Object Metadata

Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed ‘most likely trajectory’ and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub.

/pdf/process-mining-iptv-customer-eye-gaze-movement-using-16z7yldc.pdf

Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains

Customer churn prediction is a valuable task in many industries. In telecommunications it presents great challenges, given the high dimensionality of the data, and how difficult it is to identify underlying frustration signatures, which may represent an important driver regarding future churn behaviour. Here, we propose a novel Bayesian hierarchical joint model that is able to characterise customer profiles based on how many events take place within different television watching journeys, and how long it takes between events. The model drastically reduces the dimensionality of the data from thousands of observations per customer to 11 customer-level parameter estimates and random effects. We test our methodology using data from 40 BT customers (20 active and 20 who eventually cancelled their subscription) whose TV watching behaviours were recorded from October to December 2019, totalling approximately half a million observations. Employing different machine learning techniques using the parameter estimates and random effects from the Bayesian hierarchical model as features yielded up to 92% accuracy predicting churn, associated with 100% true positive rates and false positive rates as low as 14% on a validation set. Our proposed methodology represents an efficient way of reducing the dimensionality of the data, while at the same time maintaining high descriptive and predictive capabilities. We provide code to implement the Bayesian model at https://github.com/rafamoral/profiling_tv_watching_behaviour.

Ian Kegel

Papers

Draft Standard for Learning Object Metadata

Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains

Profiling Television Watching Behavior Using Bayesian Hierarchical Joint Models for Time-to-Event and Count Data

Towards automatic placement of media objects in a personalised TV experience