An OER Recommender System Supporting Accessibility Requirements

Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs

Hybrid human-AI driven open personalized education

Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer. In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer). All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result

Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners

In this paper, we suggest a novel method to aid lifelong learners to access relevant OER based learning content to master skills demanded on the labour market. Our software prototype 1) applies Text Classification and Text Mining methods on vacancy announcements to decompose jobs into meaningful skills components, which lifelong learners should target; and 2) creates a hybrid OER Recommender System to suggest personalized learning content for learners to progress towards their skill targets. For the first evaluation of this prototype we focused on two job areas: Data Scientist, and Mechanical Engineer. We applied our skill extractor approach and provided OER recommendations for learners targeting these jobs. We conducted in-depth, semi-structured interviews with 12 subject matter experts to learn how our prototype performs in terms of its objectives, logic, and contribution to learning. More than 150 recommendations were generated, and 76.9% of these recommendations were treated as useful by the interviewees. Interviews revealed that a personalized OER recommender system, based on skills demanded by labour market, has the potential to improve the learning experience of lifelong learners.Comment: This paper has been accepted to be published in the proceedings of CSEDU 2020 by SciTePres

A model for the creation of human-generated metadata within communities

This paper considers situations for which detailed metadata descriptions of learning resources are necessary, and focuses on human generation of such metadata. It describes a model which facilitates human production of good quality metadata by the development and use of structured vocabularies. Using examples, this model is applied to single and multiple communities of metadata creators. The approach for transferring vocabularies across communities is related to similar work on the use of ontologies to support the development of the semantic web. Notable conclusions from this work are the need to encourage collaboration between the metadata specialists, content authors and system designers, and the scope for using accurate and consistent metadata created for one context in another context by producing descriptions of the relationships between those contexts

Supporting lifelong learning with Open Educational Resources (OER) among diverse users: motivations for and approaches to learning with different OER

This thesis presents a study which aimed to understand: 1) What motivates and influences learning with Open Educational Resources (OER) among different users, and 2) What role OER play in supporting lifelong learning among different users. The activities of key international organisations promoting lifelong learning as significant in the context of globalisation, combined with the innovative character of OER as high-quality open learning resources, were the reasons for the focus. The aim was to understand the function of OER through exploring motivational aspects, approaches to and contexts of individual learning among users of five different OER: penLearn, OpenSpires, OpenStudy, METU OpenCourseWare and Wolne Lektury. A case study approach allowed the focus on the uniqueness of specific OER. Semi-structured interviews and virtual output collection were triangulated as data gathering methods. Interviews were analysed using the Miles and Huberman’s (1994) qualitative framework and output - using Preece et al.’s (2002) thematic analysis guidelines. Results show OER as not only resources but learning environments supporting ‘expansion of human learning’ (COL, 2011:2) through being accessible free-of-charge, openly, without registrations, exams; providing various subjects, levels and formats suitable for users with different needs, disabilities, interests and resources. OER support wider access to and inclusion in learning, empowering individuals in directing their learning, especially 9 during transitions or ‘critical periods’ (Knowles, 1973). OER users emerged as motivated by various online and offline factors related to supporting formal education or non-formal learning, and exchanging expertise or support. Even if linked to supporting formal goals, learning, participation and communication within OER are motivated intrinsically, by interest, knowledge, curiosity, enjoyment and appreciation of learning, subject or interactions. OER help foster positive attitudes to learning and teaching as flexible and innovative possibilities of skills development and re-using OER are valued by learners, educators and organisations using OER to promote their missions

Cloud eLearning - Personalisation of learning using resources from the Cloud

With the advancement of technologies, the usage of alternative eLearning systems as complementary systems to the traditional education systems is becoming part of the everyday activities. At the same time, the creation of learning resources has increased exponentially over time. However, the usability and reusability of these learning resources in various eLearning systems is difficult when they are unstandardised and semi-standardised learning resources. Furthermore, eLearning activities’ lack of suitable personalisation of the overall learning process fails to optimize resources’ and systems’ potentialities. At the same time, the evolution of learning technologies and cloud computing creates new opportunities for traditional eLearning to evolve and place the learner in the center of educational experiences. This thesis contributes to a holistic approach to the field by using a combination of artificial intelligence techniques to automatically generate a personalized learning path for individual learners using Cloud resources. We proposed an advancement of eLearning, named the Cloud eLearning, which recognizes that resources stored in Cloud eLearning can potentially be used for learning purposes. Further, the personalised content shown to Cloud Learners will be offered through automated personalized learning paths. The main issue was to select the most appropriate learning resources from the Cloud and include them in a personalised learning path. This become even more challenging when these potential learning resources were derived from various sources that might be structured, semi- structure or even unstructured, tending to increase the complexity of overall Cloud eLearning retrieval and matching processes. Therefore, this thesis presents an original concept,the Cloud eLearning, its Cloud eLearning Learning Objects as the smallest standardized learning objects, which permits reusing them because of semantic tagging with metadata. Further, it presents the Cloud eLearning Recommender System, that uses hierarchical clustering to select the most appropriate resources and utilise a vector space model to rank these resources in order of relevance for any individual learner. And it concludes with Cloud eLearning automated planner, which generates a personalised learning path using the output of the CeL recommender system

Integrated Web Accessibility Guidelines for Users on the Autism Spectrum - from Specification to Implementation

This research presented a compendium of web interface design guidelines and their implementation on a transport-planning website based on the needs and preferences of users on the autism spectrum. Results highlighted the importance of having simple navigation and meaningful headings, icons, labels and text to facilitate understanding and readability; these findings offer guidelines for the design of web user interfaces to continue improving the web experience of autistic users, and therefore of the whole community

Share.TEC Final Project Report

This report provides an overview of Share.TEC, a three-year project co-funded by the EC that supports access to, exchange and re-use of digital resources and practitioner experiences within Teacher Education at European level. The document comprises a number of sections that can either be read consecutively, to gain the full picture of the project and its outcomes, or in combinations so as to grasp particular aspects, how these were approached and what results were achieved. Section 2 describes the project\u27s overall objectives in terms of both its technological ambitions and its wider mission as part of the overall educational landscape. Section 3 gives brief profiles of the partners who made up the Share.TEC consortium. In Section 4 the results and achievements of the project are reported. This includes a description of the portal and its features; the system architecture, tools and services; the models underpinning the Share.TEC system; and the approach taken to its multilingual dimension. Section 5 addresses the question of Share.TEC\u27s target users and their needs. It describes the strategies and means employed for incorporating the user perspective, and for ensuring that the project direction was in line with users\u27 concerns so that the resulting portal responds suitably to the actual requirements of the people it\u27s designed for. Section 6 examines the critical aspect of underlying content. In keeping with the Share.TEC mission, the focus is largely on aggregated metadata records that describe digital resources for TE and which are expressed in terms defined by the project for TE purposes. Section 7 reports the activities undertaken in the project and thus narrates the processes that unfolded through the project lifetime as the consortium pursued its objectives and generated its outcomes. Section 8 describes the effort to establish the Share.TEC portal within its natural ecosystem. It looks at the global strategy for maximising impact both at regional/national level and internationally, and analyses the conditions and prospects for continuity and growth. Readers interested in the technical/technological dimension of Share.TEC (the system, portal, models, metadata, etc.) are likely to find Sections 4, 5 and 6 to be the ones closest to their concerns. Conversely, those whose interests lie elsewhere could simply consult Section 4.1 to get an idea of the portal from the user\u27s viewpoint and go to Sections 2, 3, 7 and 8 for a vision of the project and how Share.TEC is positioned in the panorama of digital resources and Teacher Education

Degree of Scaffolding: Learning Objective Metadata: A Prototype Leaning System Design for Integrating GIS into a Civil Engineering Curriculum

Digital media and networking offer great potential as tools for enhancing classroom learning environments, both local and distant. One concept and related technological tool that can facilitate the effective application and distribution of digital educational resources is learning objects in combination with the SCORM (sharable content objects reference model) compliance framework. Progressive scaffolding is a learning design approach for educational systems that provides flexible guidance to students. We are in the process of utilizing this approach within a SCORM framework in the form of a multi-level instructional design. The associated metadata required by SCORM will describe the degree of scaffolding. This paper will discuss progressive scaffolding as it relates to SCORM compliant learning objects, within the context of the design of an application for integrating Geographic Information Systems (GIS) into the civil engineering curriculum at the University of Missouri - Rolla