Learning Design for Student Retention

Student retention is an issue of increasing interest to higher education institutions, educators and students. Much of the work in this area focuses on identifying and improving interventions that occur during the presentation of a course. This paper suggests that these represent only one set of factors that can influence student withdrawal, and equally important are design based factors that can aid retention throughout the course. The main research question addressed by the paper is what design-related factors impact on student retention. An analysis of student withdrawal at the UK Open University conducted by the researchers produced a synthesis of seven key factors in the design phase that can influence retention. These factors have been given the ICEBERG acronym: Integrated, Collaborative, Engaging, Balanced, Economical, Reflective and Gradual. Examples of how these factors can be implemented are provided, and conclusions focus on how the model has been embedded in the module production process at the Open University

Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

Student Engagement in Aviation Moocs: Identifying Subgroups and Their Differences

The purpose of this study was to expand the current understanding of learner engagement in aviation-related Massive Open Online Courses (MOOCs) through cluster analysis. MOOCs, regarded for their low- or no-cost educational content, often attract thousands of students who are free to engage with the provided content to the extent of their choosing. As online training for pilots, flight attendants, mechanics, and small unmanned aerial system operators continues to expand, understanding how learners engage in optional aviation-focused, online course material may help inform course design and instruction in the aviation industry. In this study, Moore’s theory of transactional distance, which posits psychological or communicative distance can impede learning and success, was used as a descriptive framework for analysis. Archived learning analytics datasets from two 2018 iterations of the same small unmanned aerial systems MOOC were cluster-analyzed (N = 1,032 and N = 4,037). The enrolled students included individuals worldwide; some were affiliated with the host institution, but most were not. The data sets were cluster analyzed separately to categorize participants into common subpopulations based on discussion post pages viewed and posts written, video pages viewed, and quiz grades. Subgroup differences were examined in days of activity and record of completion. Pre- and postcourse survey data provided additional variables for analysis of subgroup differences in demographics (age, geographic location, education level, employment in the aviation industry) and learning goals. Analysis of engagement variables revealed three significantly different subgroups for each MOOC. Engagement patterns were similar between MOOCs for the most and least engaged groups, but differences were noted in the middle groups; MOOC 1’s middle group had a broader interest in optional content (both in discussions and videos); whereas MOOC 2’s middle group had a narrower interest in optional discussions. Mandatory items (Mandatory Discussion or Quizzes) were the best predictors in classifying subgroups for both MOOCs. Significant associations were found between subgroups and education levels, days of activity, and total quiz scores. This study addressed two known problems: a lack of information on student engagement in aviation-related MOOCs, and more broadly, a growing imperative to examine learners who utilize MOOCs but do not complete them. This study served as an important first step for course developers and instructors who aim to meet the diverse needs of the aviation-education community

Sissejuhatava programmeerimise MOOCi õppijad: taustamuutujad, kaasatuse mustrid ja õpisooritus

Väitekirja elektrooniline versioon ei sisalda publikatsiooneÜks võimalus personaalseks ja professionaalseks arenguks on osalemine vaba juurdepääsuga e-kursustel (ingl massive open online courses, MOOCs). MOOCide osalejatel on suurem autonoomia võrreldes traditsiooniliste klassiruumides toimuvate tundidega. Samuti arvestades suurt osalejate hulka ja nende erinevat tausta, on kõikide õppijate kaasatus (ingl engagement) õppeprotsessis MOOCide korraldajatele väljakutseks. Osalejate taustamuutujate (ingl background variables) mõju kaasatusele, mis omakorda võib mõjutada õpisooritust (ingl performance), on jätkuvalt alauuritud valdkond. Doktoritöö eesmärk oli uurida MOOCide osalejate taustamuutujaid ja nende mõju kursusele registreerumisele ning lõpetamise tõenäosusele, tuvastada lõpetajate seas käitumuslikke (ingl behavioural engagement) ja kognitiivseid (ingl cognitive engagement) kaasatuse rühmasid ning uurida neid taustamuutujate ja õpisoorituse osas. Uurimuse fookuses oli MOOC “Programmeerimisest maalähedaselt”. Selle MOOCi osalejate ja lõpetajate taustamuutujad võrreldi MOOCidega „Programmeerimise alused I“ ja „Programmeerimise alused II“. MOOCil “Programmeerimisest maalähedaselt” oli rohkem naisi ja neid, kelle haridustase oli madalam. Lõpetajate osas selgus, et põhifookuses olnud MOOCil, ei olnud statistiliselt olulist erinevust nais- ja meeslõpetajate osakaalu ning erinevate tööhõive staatuste vahel. Suurem lõpetajate osakaal oli magistrikraadiga lõpetajate hulgas. Väiksem lõpetajate osakaal oli nende õppijate puhul, kes ei ole varem programmeerimist õppinud. Samad tulemused lõpetajate kohta olid ka MOOCil “Programmeerimise alused I“. Uurides MOOCi “Programmeerimisest maalähedaselt” lõpetajate ja mittelõpetajate õpisooritust, selgus, et nad vajasid testi sooritamiseks keskmiselt sama palju katseid. Mittelõpetajatel oli programmeerimisülesannete lahenduste esitamiskordade arv suurem ja neil oli testipunktid madalamad. Lõpetajate käitumusliku ja kognitiivse kaasatuse analüüs näitas, et lõpetajad ei ole homogeenne rühm. Käitumusliku kaasatuse puhul eristati lähtudes tegevuste hulgast 4 rühma. Uurimuse tulemused näitasid, et MOOCil võivad olla lõpetajad, kes teevad kõiki tegevusi, aga ka need, kes teevad vaid mõnda tegevust. Kognitiivse kaasatuse korral eristus 5 rühma, mille puhul kasutati abiallikaid erineva sagedusega. Tulemused näitasid, et lõpetajate erinevat sagedust erinevate abiallikate kasutamisel võib pidada märgiks püsivast soovist MOOC edukalt läbida. Samuti selgus, et abiallikate kasutamise võib võtta aluseks kognitiivse kaasatuse tuvastamiseks ja mõõtmiseks MOOCidel. Lõpetajate taustamuutujad ja õpisooritused varieerusid eristatud rühmade vahel. Doktoritöös esitatud tulemused aitavad uurijatel paremini aru saada MOOCi fenomenist ja kursuste korraldajatel pakkuda tulevikus kulutõhusamaid MOOCe. Uurimistulemustest võib järeldada, et MOOCide korraldajad peavad pakkuma erinevaid tegevusi ja abiallikaid, mis oleksid suunatud konkreetsetele sihtrühmadele. See võib hõlbustada personaliseeritud õppimist ja õppijate tõhusat kaasatust õppeprotsessis.One opportunity to facilitate personal and professional development is to participate in massive open online courses (MOOCs). MOOCs participants have greater autonomy compared to traditional physical classes. In addition, considering the huge number of participants and diversity of their backgrounds, it is a challenge for MOOCs instructors to engage them all in learning. The impact of background variables on engagement, which in turn may influence performance, remains understudied. The doctoral thesis aimed to study MOOCs participants’ background variables and their impact on course enrolment and completion probability, and explore different behavioural and cognitive engagement clusters among completers in terms of background variables and performance. The thesis focused on a MOOC “About Programming”. The MOOC participants’ and completers’ background variables were examined in comparison to MOOCs “Introduction to Programming I” and “Introduction to Programming II”. Females and those with a lower education level dominated in the MOOC “About Programming”. In this course, among completers there was no difference by gender and employment statuses. Master’s degree holders were more likely to complete the MOOC, while inexperienced in programming were less likely to complete it. The same results about completers were found in the MOOC “Introduction to Programming I”. With regard to performance, no difference between the MOOC “About programming” completers and non-completers in the average number of attempts per quiz was found. But non-completers made on average more attempts per programming task and received lower scores per quiz. The analysis of behavioural and cognitive engagement solely among completers indicated that they cannot be considered a homogeneous group. In terms of behavioural engagement, there were identified 4 groups based on the amount of activities a completer engaged with during the MOOC. The study results indicated that in a MOOC there can be completers who engage with all activities, as well as those who engage with only a few activities. In terms of cognitive engagement, there were identified 5 groups that were engaged with help sources at different frequency. The results indicated that the different frequency, with which completers use different help sources, can be considered as a sign of persistent desire to successfully complete the MOOC. In addition, it was revealed that the use of help sources can be applied as a basis for identifying and measuring cognitive engagement in the MOOC context. The background variables and performance of completers from different identified groups varied. The results of the thesis can prove quite beneficial to the scientific literature to understand the phenomenon of MOOC. This comprehension in terms of a variety of background variables, engagement patterns and performance can be helpful for course instructors to develop cost-effective MOOCs and provide personalised learning where different course activities and help sources can be targeted at specific groups.  https://www.ester.ee/record=b552843

Chapter 38 Learning Analytics

In this chapter, we present an overview of the field by articulating definitions and existing models of learning analytics. Case examples of learning analytics from Asian researchers are then summarized and reported. This is followed by an exploration of the key tensions in this field. The chapter concludes with a discussion of potential areas for future research in this area

An analysis of students’ behaviour in a Learning Management System through Process Mining

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe exponential growth and transformation of the Internet and information technology in recent years led to the development of several analytical tools. As is the case with process mining, it emerged to fulfill the need to extract and analyze information from event logs by representing it in the form of process models. Process mining is an acclaimed tool and proved crucial in several areas, from healthcare to manufacturing and finance. Nevertheless, and despite the crucial role of digital systems in supporting learning activities and generating large amounts of data about learning processes, limited research focused on process mining applied to the educational context. Therefore, the aim of this dissertation is to apply a process-oriented approach and demonstrate the applicability of process mining techniques to explore and analyze students’ behavior and interaction patterns, based on data collected from Moodle, the widely used Learning Management System. We cover definitions of process mining, education, and a detailed search of the existing literature on educational process mining during this work. Furthermore, the paper analyzes and discusses the findings of the study that combines process mining techniques, specifically process discovery implanted in the Disco tool, with cluster analysis. Through the application of these two techniques, it was possible to recognize the relationship between the students’ behavior registered in the process models and the success of the students in the course, along with the general and specific information about the students’ learning paths. Besides, we obtained findings that allow us to predict the group of students at risk of failing. Finally, with the analysis of these results, we were able to provide improvement proposals and recommendations to enhance the learning experience

Investigating Variation in Learning Processes in a FutureLearn MOOC

Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of engagement in the course. This study investigates the detailed processes of engagement using educational process mining (EPM) in a FutureLearn science course (N = 2086 learners) and applying an established taxonomy of learning design to classify learning activities. The analyses were performed on three groups of learners categorised based upon their clicking behaviour. The process-mining results show at least one dominant pathway in each of the three groups, though multiple popular additional pathways were identified within each group. All three groups remained interested and engaged in the various learning and assessment activities. The findings from this study suggest that in the analysis of voluminous MOOC data there is value in first clustering learners and then investigating detailed progressions within each cluster that take the order and type of learning activities into account. The approach is promising because it provides insight into variation in behavioural sequences based on learners’ intentions for earning a course certificate. These insights can inform the targeting of analytics-based interventions to support learners and inform MOOC designers about adapting learning activities to different groups of learners based on their goals