A conceptual framework for learners self-directing their learning in MOOCs: components, enablers and inhibitors

The conceptual framework presented in this chapter describes the learning components influencing the learning experiences of adult informal learners engaged in MOOCs offered on the FutureLearn platform. It consists of five learning components: individual characteristics, technology, individual & social learning, organising learning, and context. These five learning components are driven by two enablers or inhibitors of learning: motivation and learning goals. For adult informal learners, motivation is mostly intrinsic, and learning goals are mostly personal. This research investigated the informal learning of 56 adult learners with prior online experience, as they studied various subjects in MOOCs. Literature on MOOCs, mobile and informal learning provides scientific support, in addition to literature clarifying the rationale for self-directed learning as a focus of investigation. The participants of this study voluntarily followed one of three FutureLearn courses that were rolled out for the first time at the end of 2014. Data were collected at three stages through an online survey (pre-course), self-reported learning logs (during the course), and semi-structured one-on-one interviews (post-course). The data were analysed using Charmaz’s (2014) method for constructing a grounded theory

Elderly learners and Massive Open Online Courses: a review

Background: Massive open online courses (MOOCs) have become commonplace in the e-learning landscape. Thousands of elderly learners are participating in courses offered by various institutions on a multitude of platforms in many different languages. However, there is very little research into understanding elderly learners in MOOCs. Objective: We aim to show that a considerable proportion of elderly learners are participating in MOOCs and that there is a lack of research in this area. We hope this assertion of the wide gap in research on elderly learners in MOOCs will pave the way for more research in this area. Methods: Pre-course survey data for 10 University of Reading courses on the FutureLearn platform were analyzed to show the level of participation of elderly learners in MOOCs. Two MOOC aggregator sites (Class Central and MOOC List) were consulted to gather data on MOOC offerings that include topics relating to aging. In parallel, a selected set of MOOC platform catalogues, along with a recently published review on health and medicine-related MOOCs, were searched to find courses relating to aging. A systematic literature search was then employed to identify research articles on elderly learners in MOOCs. Results: The 10 courses reviewed had a considerable proportion of elderly learners participating in them. For the over-66 age group, this varied from 0.5% (on the course “Managing people”) to 16.3% (on the course “Our changing climate”), while for the over-56 age group it ranged from 3.0% (on “A beginners guide to writing in English”) to 39.5% (on “Heart health”). Only six MOOCs were found to include topics related to aging: three were on the Coursera platform, two on the FutureLearn platform, and one on the Open2Study platform. Just three scholarly articles relating to MOOCs and elderly learners were retrieved from the literature search. Conclusions: This review presents evidence to suggest that elderly learners are already participating in MOOCs. Despite this, there has been very little research into their engagement with MOOCs. Similarly, there has been little research into exploiting the scope of MOOCs for delivering topics that would be of interest to elderly learners. We believe there is potential to use MOOCs as a way of tackling the issue of loneliness among older adults by engaging them as either resource personnel or learners

Learners Self-directing Learning in FutureLearn MOOCs: A Learner-Centered Study

This qualitative research study focuses on how experienced online learners self-direct their learning while engaging in a MOOC delivered on the FutureLearn platform. Self-directed learning is an important concept within informal learning and online learning. This study distinguishes itself from previous MOOC learner studies, by reporting the self-directed learning using a bottom-up approach. By looking at self-reported learning logs and interview transcripts an in-depth analysis of the self-directed learning is achieved. The data analysis used constructed grounded theory, which aligns with the bottom-up approach where the learner data is coded and investigated in an open, yet evidence-based way, leaving room for insights to emerge from the learner data. The data corpus is based on 56 participants following three FutureLearn MOOCs, providing 147 learning logs and 19 semi-structured one-on-one interviews with a selection of participants. The results show five specific areas in which learners react with either the material or other learners to self-direct their learning: context, individual or social learning, technology and media provided in the MOOCs, learner characteristics and organising learning. This study also indicates how intrinsic motivation and personal learning goals are the main inhibitors or enablers of self-directed learning

Personalisation in MOOCs: a critical literature review

The advent and rise of Massive Open Online Courses (MOOCs) have brought many issues to the area of educational technology. Researchers in the field have been addressing these issues such as pedagogical quality of MOOCs, high attrition rates, and sustainability of MOOCs. However, MOOCs personalisation has not been subject of the wide discussions around MOOCs. This paper presents a critical literature survey and analysis of the available literature on personalisation in MOOCs to identify the needs, the current states and efforts to personalise learning in MOOCs. The findings illustrate that there is a growing attention to personalisation to improve learners’ individual learning experiences in MOOCs. In order to implement personalised services, personalised learning path, personalised assessment and feedback, personalised forum thread and recommendation service for related learning materials or learning tasks are commonly applied

An Anatomy Massive Open Online Course as a Continuing Professional Development Tool for Healthcare Professionals

Massive open online courses (MOOCs) remain a novel and under-evaluated learning tool within anatomical and medical education. This study aimed to provide valuable information by using an anatomy MOOC to investigate the demographic profile, patterns of engagement and self-perceived benefits to healthcare professionals. A 21-item survey aimed at healthcare professionals was embedded into the Exploring Anatomy: The Human Abdomen MOOC, in April 2016. The course attracted 2711 individual learners with 94 of these completing the survey, and 79 of those confirming they worked full- or part-time as healthcare professionals. Variations in use across healthcare profession (allied healthcare professional, nurse or doctor) were explored using a Fisher’s exact test to calculate significance across demographic, motivation and engagement items; one-way ANOVA was used to compare self-perceived benefits. Survey data revealed that 53.2% were allied healthcare professionals, 35.4% nurses and 11.4% doctors. Across all professions, the main motivation for enrolling was to learn new things in relation to their clinical practice, with a majority following the prescribed course pathway and utilising core, and clinically relevant, material. The main benefits were in relation to improving anatomy knowledge, which enabled better support for patients. This exploratory study assessing engagement and self-perceived benefits of an anatomy MOOC has shown a high level of ordered involvement, with some indicators suggesting possible benefits to patients by enhancing the subject knowledge of those enrolled. It is suggested that this type of learning tool should be further explored as an approach to continuing professional, and interprofessional, education

Effect of acceptable use policy on employee computer use : case of Sri Lankan software development organizations

CD-Rom included ; A Dissertation submitted to the Department of Computer Science and Engineering for the MBAABSTRACT It is a known fact that some employees misuse the organizational computers to do their personal work such as sending emails, surfing the Internet, chatting, playing games. These activities not only waste productive time of employees but also bring a risk factor to the organization. This affects organizations in the software industry very much as almost all of their employees are connected to the Internet throughout them day./ By introducing an Acceptable Use Policy (AUP) for an organization, it is believed that the computer misuse by its employees could be reduced. In many countries Acceptable Use Policies are used and they have been studied with various perspectives. In Sri Lankan context research on these areas are scarce. This research explored the situation in Sri Lanka with respect to AUPs and their effectiveness./ A descriptive study was carried out to identify the large and medium scale software development organizations that had implemented computer usage guidelines for employees. A questionnaire was used to gather information regarding employee’s usual computer usage behavior. Stratified random sampling was employed to draw a representative sample from the population./ Majority of the organizations have not employed a written guideline on acceptable use of work computers. The study results did not provide evidence to conclude that the presence or non presence of an AUP has a significant difference in computer use behaviors of employees. A significant negative correlation was observed between level of awareness about AUP and misuse. Access to the Internet and organizational settings were identified as significant factors that influence employee computer misuse behavior

The [Un]Democratisation of Education and Learning

OCs have engendered excitement around their potential to democratise education. They appear to act as a leveller and offer equal opportunity to millions of learners worldwide. Yet, this alluring promise is not wholly achieved by MOOCs. The courses are designed to be used by people who are already able to learn, thereby excluding learners who are unable to learn without direct tutor support. The solutions to this problem tend to focus on the course, as ‘learning design’ or ‘learning analytics’. We argue that effort needs to be focused on the learner directly, supporting him or her to become an autonomous learner. Supporting millions of people to become autonomous learners is complex and costly. This is a problem where education is shaped principally by economic and neoliberal forces, rather than social factors. However, ‘automated’ solutions may result in attempts to quantify learners’ behaviours to fit an ‘ideal’. There is a danger that overly simplified solutions aggravate and intensify inequalities of participation

MOOC dropouts: A multi-system classifier

In recent years, technology enhanced learning platforms became widely accessible. In particular, the number of Massive Open Online Courses (MOOCs) has—and still is—constantly growing. This widespread adoption of MOOCs triggered the development of specialized solutions, that emphasize or enhance various aspects of traditional MOOCs. Despite this significant diversity in approaches to implementing MOOCs, many of the solutions share a plethora of common problems. For example, high dropout rate is an on-going problem that still needs to be tackled in the majority of MOOCs. In this paper, we set out to analyze dropout problem for a number of different systems with the goal of contributing to a better understanding of rules that govern how MOOCs in general and dropouts in particular evolve. To that end, we report on and analyze MOOCs from Universidad Galileo and Curtin University. First, we analyze the MOOCs of each system independently and then build a model and predict dropouts across the two systems. Finally, we identify and discuss features that best predict if users will drop out or continue and complete a MOOC using Boosted Decision Trees. The main contribution of this paper is a unified model, which allows for an early prediction of at-risk or dropout users across different systems. Furthermore, we also identify and discuss the most indicative features of our model. Our results indicate that users’ behaviors during the initial phase of MOOCs relate to their final results