Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin

Retention and progression of online global students: a pilot approach

Higher education institutions are making increasing use of online course delivery as part of their standard offering. E-learning can support the move toward global student bodies and the possibility of more responsive teaching and learning environments. The Open University Business School has offered online distance learning courses for over 10 years and supports thousands of students each year. As student numbers have grown, the capacity to provide truly personalised academic, pastoral and administrative student support is clearly affected. This case study describes a pilot approach to delivering more intelligent and proactive intervention to students registered on an online, open entry, level 3 undergraduate programme. We briefly outline the programme and existing comparative data on known differences between the retention and final achievements of students receiving support solely online compared to those receiving a more traditional blended means of course delivery and tuition support. The study goes on to describe the developing work of the pilot team in setting in place a number of key interventions thought most likely to support the student through their study journey and optimise their chances of completion. The Open University in the UK, like other HE institutions, knows a great deal about its students before they start to study, and, perhaps like others, has not always fully exploited this information. The pilot team is now using profiling data to identify key student characteristics which suggest that additional pre-course contact would be helpful. This may be a discussion of how we might best support the student whilst on course, or may include advice about transferring to another course more suited to their experience or circumstances given the open entry nature of the courses.Systems have been developed and refined which allow the team to track student behaviour once the course has begun, and since the courses within the pilot make heavy use of a Moodle-based Virtual Learning Environment (VLE), there is much that is transparent to us. Each course has a number of defined milestones which have been agreed to be key or at least facilitative to the students' eventual completion and success. Our systems help us to work closely with course tutors and students to trigger additional contacts from the support team. Other support activities are designed to complement this ongoing work and will be described more fully in the paper. It is crucial that all of the work has the potential for automation and scalability – currently the pilot team is working with over 800 students in around 30 countries. This paper aims to demonstrate that the piloted levels of intervention are both achievable in the long term and cost-effective. Results from the first 2 pilot presentations will be shared alongside results from a comparator cohort

Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches

Perceived School Style and Academic Outcomes among Ethnically Diverse College Students

Students’ perceptions of their schools play an important role in achievement. One framework for measuring students’ perceptions is an adaptation of Baumrind’s parenting typology, which measures perceived “school style” (Pellerin, 2005) along two dimensions of responsiveness (warmth) and demandingness (high academic expectations). Although research suggests that perceptions of authoritative styles (both responsive and demanding) correlate with better student outcomes (Dornbusch et al., 1987), no existing research has considered whether these findings apply to ethnically diverse samples. We surveyed 301 students from five Midwestern colleges who completed measures of perceived school style, perceived discrimination, and several academic outcomes. Academically stigmatized students (African Americans and Latinos) perceived similar levels of demandingness but significantly lower levels of responsiveness from their instructors than did their non-stigmatized peers. Importantly, perceived discrimination in college fully mediated this relationship. With regard to the academic outcome variables, we found a significant interaction between responsiveness and demandingness such that only students who perceived high levels of both showed higher levels of attendance and out-of-class engagement. Finally, we found a significant three-way interaction between responsiveness, demandingness, and academic minority status in predicting academic efficacy. High levels of responsiveness and demandingness were related to increased academic efficacy only for non-academically stigmatized students. These results imply not only that the benefits of perceived school responsiveness and demandingness often depend on one another, but also that these benefits do not always apply equally to all students

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

Towards a System of Guidance, Assistance and Learning Analytics Based on Multi Agent System Applied on Serious Games

With the revolution that the education field has known concerning the methods of learning and especially the integration of new technology, several new tools have appeared to replace the tools already existing, and among them there are serious games, serious games as new tool dedicated to education have occupied an important place, and replaced other tools often used in the learning process. But in the order that serious games reach the intended objectives and help instructors to achieve their perspectives considered, they must be equipped with a guidance and assistance system that will assist the learners during the progression in the sequence of the video game, and in addition, they must be equipped with a system of learning analytics that will help instructors to improve the learning process and teaching methods according to the learning outcomes and feedbacks of their learners. In this perspective of research and development we will establish in this paper a new system of assistance, guidance and learning analytics based on a multi agent system that will work in tandem with a web-based serious game

Investigating the Efficacy of Algorithmic Student Modelling in Predicting Students at Risk of Failing in the Early Stages of Tertiary Education: Case study of experience based on first year students at an Institute of Technology in Ireland.

The application of data analytics to educational settings is an emerging and growing research area. Much of the published works to-date are based on ever-increasing volumes of log data that are systematically gathered in virtual learning environments as part of module delivery. This thesis took a unique approach to modelling academic performance; it is a first study to model indicators of students at risk of failing in first year of tertiary education, based on data gathered prior to commencement of first year, facilitating early engagement with at-risk students. The study was conducted over three years, in 2010 through 2012, and was based on a sample student population (n=1,207) aged between 18 and 60 from a range of academic disciplines. Data was extracted from both student enrolment data maintained by college administration, and an online, self-reporting, learner profiling tool developed specifically for this study. The profiling tool was administered during induction sessions for students enrolling into the first year of study. Twenty-four factors relating to prior academic performance, personality, motivation, self-regulation, learning approaches, learner modality, age and gender were considered. Eight classification algorithms were evaluated. Cross validation model accuracies based on all participants were compared with models trained on the 2010 and 2011 student cohorts, and tested on the 2012 student cohort. Best cross validation model accuracies were a Support Vector Machine (82%) and Neural Network (75%). The k-Nearest Neighbour model, which has received little attention in educational data mining studies, achieved highest model accuracy when applied to the 2012 student cohort (72%). The performance was similar to its cross validation model accuracy (72%). Model accuracies for other algorithms applied to the 2012 student cohort also compared favourably; for example Ensembles (71%), Support Vector Machine (70%) and a Decision Tree (70%). Models of subgroups by age and by academic discipline achieved higher accuracy than models of all participants, however, a larger sample size is needed to confirm results. Progressive sampling showed a sample size \u3e 900 was required to achieve convergence of model accuracy. Results showed that factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance and self-efficacy. Kinaesthetic modality was also indicative of students at risk of failing, a factor that has not been cited previously as a significant predictor of academic performance. Models reported in this study show that learner profiling completed prior to commencement of first year of study yielded informative and generalisable results that identified students at risk of failing. Additionally, model accuracies were comparable to models reported elsewhere that included data collected from student activity in semester one, confirming the validity of early student profiling