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

Identification of Affective States in MOOCs: A Systematic Literature Review

Massive Open Online Courses (MOOCs) are a type of online coursewere students have little interaction,  no instructor, and in some cases, no deadlines to finisch assignments. For this reason, a better understanding of student affection in MOOCs is importantant could have potential to open new perspectives for this type of course. The recent popularization of tools, code libraries and algorithms for intensive data analysis made possible collect data from text and interaction with the platforms, which can be used to infer correlations between affection and learning. In this context, a bibliographical review was carried out, considering the period between 2012 and 2018, with the goal of identifying which methods are being to identify affective states. Three databases were used: ACM Digital Library, IEEE Xplore and Scopus, and 46 papers were found. The articles revealed that the most common methods are related to data intensive techinques (i.e. machine learning, sentiment analysis and, more broadly, learning analytics). Methods such as physiological signal recognition andself-report were less frequent

Predicting Paid Certification in Massive Open Online Courses

Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

A conceptual model for e-learning supporting tools design based on cue model and Kansei engineering

The Covid-19 pandemic has triggered changes in learning due to the practice of social distancing to curb the spread of the virus. E-learning platforms have become the main platform for learning throughout the pandemic. However, e-learning does have challenges when it comes to ensuring student’s optimum participation throughout the learning experience that require extensive research about techniques and methods for an optimum e-learning experience. This includes various e-learning supporting tools that provides easy communication and immediate assistance to enhance user experience. The supporting tools or software usability and functionality design determined as imperative in enhancing the e-learning user experience. Thus, this research proposes a conceptual model for designing the e-learning supporting tools based on the CUE Model, integrated with Kansei Engineering for optimum user experience that can serve as a guideline for the e-learning supporting tools designer. The outcome of this research will create new research fields that incorporate multiple domains, including the e-learning domain, software and supporting tools design, emotions and user experience

Interplaying factors of students personal characteristics in online learning modality: evidence in asian context

Mapping the multidimensional impact of learner attributes on behavior demonstrates the importance of models in learning. To this purpose, we examined the correlations between strategies and student characteristics and utilized regression analysis to determine how learner attributes affect strategy selection. A cross-sectional study of 258 students demonstrated widespread strategy use, as well as statistically significant connections within and between the Strategy Inventory for Language Learning and Student Characteristics of Learning measures. Regression analysis found distinctions in the types of learner characteristics associated with strategy adoption, most notably between direct and indirect strategies. Instrumental motivation predicted both direct and indirect Strategy Inventory for Language Learning scores, but self-efficacy affected memory, cognitive, and compensatory strategies, and perseverance predicted reported metacognitive and emotional strategy choice levels. Additionally, a negative route coefficient occurred between persistence and compensation techniques and between competition and memory strategies, implying mediation and a high degree of complexity in the way learner traits impact behavior. The present study's findings have implications for prospective instructor techniques for motivating students to become fully involved in language learning via the online procedure.Campus At

Exploring the Effects of Dynamic Avatar on Performance and Engagement in Educational Games

Avatar research has almost exclusively explored avatars that remain the same regardless of context. However, there may be advantages to avatars that change during use. A plethora of work has shown that avatars personalized in one’s likeness increases identification, while object-like avatars increase detachment. We posit that in certain situations within a game it may be more advantageous to have increased identification, while in other situations increased detachment. We present a study on dynamic avatars, or avatars that change types based on game context. In particular, we investigate what we term the successful likeness avatar. The successful likeness is an avatar that is only a likeness when the player is in a win state and at all other times an object. Our goal is to determine if this type of avatar can foster an increase in user performance and engagement. Our experiment (N=997) compares four avatars: 1) Shape, 2) Likeness, 3) Likeness to Shape, and 4) Shape to Likeness (successful likeness). We found that players using a successful likeness avatar had significantly better performance (levels completed) than all other conditions. Players using a successful likeness avatar had significantly higher play time (minutes played) than all other conditions. We propose a theoretical model in which identification facilitates vicarious outcomes and in which detachment facilitates outcome dissociation. As performance and engagement are correlated to learning (Harteveld, 2015), successful likeness avatars may be crucial in educational games.National Science Foundation (U.S.) (STEM+C Grant 1542970)Natural Sciences and Engineering Research Council of Canada (Fellowship

ROLE OF PERSONAL ATTRIBUTES AND SYSTEM CHARACTERISTICS IN PREDICTING THE EFFECTIVENESS OF ONLINE LEARNING- AN INDIAN PERSPECTIVE

Online learning has become a trend in education over the years with the emergence of Web 2.0 and the advancement in Information and Communication Technologies (ICT). As the organisational spending has risen for providing better learning and training, the expectations for outcomes also have increased. Learning effectiveness can be thought of as one of the parameters to assess the success of online learning. A survey was conducted with 377 higher education students from India who have already taken an online learning course. The study used Structural Equation Modelling (SEM) to understand the impact of personal factors (internet self-efficacy), system characteristics (information quality, system quality, service quality), and engagement (behavioural, emotional, cognitive engagement) on learning effectiveness in online learning through an integration of Social Cognitive Theory (SCT), and DeLone and McLean’s IS success model. The result shows that internet self-efficacy has a positive impact on all types of engagement whereas, system and service quality have a positive impact on emotional and cognitive engagement, and information quality has an impact on only behavioural engagement. Furthermore, all types of engagement have a positive impact on perceived learning effectiveness. Theoretical contributions and practical implications are discussed

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

The future of employee development

A series of trends shaping the current workplace has changed the nature of human capital development practice to be more employee-driven. However, existing development research does not fully account for this shift and the anticipated benefits of employee-driven development. In this review we reflect on the current state of the employee development literature and propose a new, broader conceptualization of employee development characterized by a partnership between the employer and employee. In doing so, we offer three recommendations for how research needs to evolve to align employee development scholarship with current practices: (1) incorporate proactivity in the definition of employee development, (2) update the context for learning, and, (3) think differently about how human capital is valued. We suggest ways in which theory can be extended for increasing our understanding of several commonly used employee-driven development methods. Finally, we provide future research questions and practical suggestions based on our new conceptualization of employee development