Beyond Prediction: First Steps Toward Automatic Intervention in MOOC Student Stopout

ABSTRACT High attrition rates in massive open online courses (MOOCs) have motivated growing interest in the automatic detection of student "stopout". Stopout classifiers can be used to orchestrate an intervention before students quit, and to survey students dynamically about why they ceased participation. In this paper we expand on existing stop-out detection research by (1) exploring important elements of classifier design such as generalizability to new courses; (2) developing a novel framework inspired by control theory for how to use a classifier's outputs to make intelligent decisions; and (3) presenting results from a "dynamic survey intervention" conducted on 2 HarvardX MOOCs, containing over 40000 students, in early 2015. Our results suggest that surveying students based on an automatic stopout classifier achieves higher response rates compared to traditional post-course surveys, and may boost students' propensity to "come back" into the course

Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect

Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes

Artificial Intelligence methodologies to early predict student outcome and enrich learning material

L'abstract è presente nell'allegato / the abstract is in the attachmen

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

info:eu-repo/semantics/publishedVersio

Analytics-based approach to the study of learning networks in digital education settings

Investigating howgroups communicate, build knowledge and expertise, reach consensus or collaboratively solve complex problems, became one of the main foci of contemporary research in learning and social sciences. Emerging models of communication and empowerment of networks as a form of social organization further reshaped practice and pedagogy of online education, bringing research on learning networks into the mainstream of educational and social science research. In such conditions, massive open online courses (MOOCs) emerged as one of the promising approaches to facilitating learning in networked settings and shifting education towards more open and lifelong learning. Nevertheless, this most recent educational turn highlights the importance of understanding social and technological (i.e., material) factors as mutually interdependent, challenging the existing forms of pedagogy and practice of assessment for learning in online environments. On the other hand, the main focus of the contemporary research on networked learning is primarily oriented towards retrospective analysis of learning networks and informing design of future tasks and recommendations for learning. Although providing invaluable insights for understanding learning in networked settings, the nature of commonly applied approaches does not necessarily allow for providing means for understanding learning as it unfolds. In that sense, learning analytics, as a multidisciplinary research field, presents a complementary research strand to the contemporary research on learning networks. Providing theory-driven and analytics-based methods that would allow for comprehensive assessment of complex learning skills, learning analytics positions itself either as the end point or a part of the pedagogy of learning in networked settings. The thesis contributes to the development of learning analytics-based research in studying learning networks that emerge fromthe context of learning with MOOCs. Being rooted in the well-established evidence-centered design assessment framework, the thesis develops a conceptual analytics-based model that provides means for understanding learning networks from both individual and network levels. The proposed model provides a theory-driven conceptualization of the main constructs, along with their mutual relationships, necessary for studying learning networks. Specifically, to provide comprehensive understanding of learning networks, it is necessary to account for structure of learner interactions, discourse generated in the learning process, and dynamics of structural and discourse properties. These three elements – structure, discourse, and dynamics – should be observed as mutually dependent, taking into account learners’ personal interests, motivation, behavior, and contextual factors that determine the environment in which a specific learning network develops. The thesis also offers an operationalization of the constructs identified in the model with the aim at providing learning analytics-methods for the implementation of assessment for learning. In so doing, I offered a redefinition of the existing educational framework that defines learner engagement in order to account for specific aspects of learning networks emerging from learning with MOOCs. Finally, throughout the empirical work presented in five peer-reviewed studies, the thesis provides an evaluation of the proposed model and introduces novel learning analytics methods that provide different perspectives for understanding learning networks. The empirical work also provides significant theoretical and methodological contributions for research and practice in the context of learning networks emerging from learning with MOOCs

Improving Mobile MOOC Learning via Implicit Physiological Signal Sensing

Massive Open Online Courses (MOOCs) are becoming a promising solution for delivering high- quality education on a large scale at low cost in recent years. Despite the great potential, today’s MOOCs also suffer from challenges such as low student engagement, lack of personalization, and most importantly, lack of direct, immediate feedback channels from students to instructors. This dissertation explores the use of physiological signals implicitly collected via a "sensorless" approach as a rich feedback channel to understand, model, and improve learning in mobile MOOC contexts. I first demonstrate AttentiveLearner, a mobile MOOC system which captures learners' physiological signals implicitly during learning on unmodified mobile phones. AttentiveLearner uses on-lens finger gestures for video control and monitors learners’ photoplethysmography (PPG) signals based on the fingertip transparency change captured by the back camera. Through series of usability studies and follow-up analyses, I show that the tangible video control interface of AttentiveLearner is intuitive to use and easy to operate, and the PPG signals implicitly captured by AttentiveLearner can be used to infer both learners’ cognitive states (boredom and confusion levels) and divided attention (multitasking and external auditory distractions). Building on top of AttentiveLearner, I design, implement, and evaluate a novel intervention technology, Context and Cognitive State triggered Feed-Forward (C2F2), which infers and responds to learners’ boredom and disengagement events in real time via a combination of PPG-based cognitive state inference and learning topic importance monitoring. C2F2 proactively reminds a student of important upcoming content (feed-forward interventions) when disengagement is detected. A 48-participant user study shows that C2F2 on average improves learning gains by 20.2% compared with a non-interactive baseline system and is especially effective for bottom performers (improving their learning gains by 41.6%). Finally, to gain a holistic understanding of the dynamics of MOOC learning, I investigate the temporal dynamics of affective states of MOOC learners in a 22 participant study. Through both a quantitative analysis of the temporal transitions of affective states and a qualitative analysis of subjective feedback, I investigate differences between mobile MOOC learning and complex learning activities in terms of affect dynamics, and discuss pedagogical implications in detail

Influence of remaining unmet financial need on the persistence behaviors of students enrolled at a small, liberal arts institution of higher education

This single institution, quantitative study examined the degree to which remaining unmet financial need affected both 2nd fall and 3rd fall persistence measures at a small, private, religiously affiliated, liberal arts university in the southeastern United States. The purpose of this research was to contribute to the literature on college persistence and explore the complex world of how students finance their college education. A hierarchical logistic regression analysis was used to determine if the control variables (entry year, gender, race/ethnicity) and independent variables (high school GPA (HSGPA) and remaining unmet financial need (RUFN)) were significant contributors to models that predicted both 2nd fall (3rd semester) and 3rd fall (5th semester) persistence. The findings of this study suggest that RUFN was a statistically significant predictor of both 2nd fall and 3rd fall persistence, as was HSGPA. The control variables were all nonsignificant in models that included HSGPA or RUFN. The implications of knowing a student’s RUFN are provided, along with recommendations for future research involving higher education leadership and RUFN

Self-Determination Theory and MOOC enrollment motivation: Validation of the online learning enrollment intentions scale

Personalized learning tracks within MOOCs remain underdeveloped. Despite MOOCs possessing tremendous potential for personalized learning, little individualization of the MOOC has occurred. Some students look at MOOCs as a textbook, others as a formal course, and others as an opportunity to socialize. Understanding student enrollment needs are a critical initial step to helping students get the most out of MOOC. The online learning enrollment intentions scale (OLEI) (Kizilcec & Schneider, 2015) inventories student enrollment motivation in MOOCs. Despite being a short measure of enrollment motivation, the OLEI has not been widely deployed in MOOCs or reported in MOOC literature. This investigation contributes to the validity and reliability evidence of the OLEI by correlating it with mature instruments based on Self-Determination Theory (SDT) and Self-Regulated Learning (SRL). SDT posits that humans are moved by intrinsic, extrinsic, and social motivations. SDT also asserts that the absence of motivation is amotivation. Amotivation is an important construct to study in MOOCs. The overwhelming majority of students sign up for MOOCs and fail to pursue significant learning beyond initial sign-up. Understanding this amotivational state is necessary to developing interventions that motivate MOOC learners to return to the course before they fail to satisfy their learning goals. These four motivational states (intrinsic, extrinsic, social, and amotivational) were investigated using quantitative methods. This study used correlation coefficients to compare associations between the OLEI and instruments built on SDT and SRL. Sixty-eight participants were solicited from active MOOCs on the edX and Coursera platforms. Results support that the OLEI accurately inventories extrinsic and amotivational initial enrollment states. Less support was reported with validity associations for intrinsic or social motivational states and the OLEI. Validity and reliability evidence for the OLEI is reported