Investigating Engagement and Learning Differences between Native and EFL students in Active Video Watching

Video-based learning (VBL) requires good listening and reading comprehension skills, which could be challenging for English as a foreign language (EFL) students. In this paper, we investigate the differences between EFL and Native English speakers in a VBL platform called AVW-Space, in order to identify potential interventions that would be helpful for EFL students. AVW-Space provides note-taking, peer-reviewing, visualisations and personalised nudges to support engagement in VBL. Although previous studies on AVW-Space showed these supports were effective for increasing engagement, we discovered significant differences in learning outcomes and engagement between EFL/Native students, which stem from different learning strategies, background knowledge and language barriers. This research contributes to using learning analytics to understand better the differences between EFL and Native students, and providing more specialised support for EFL students in VBL

Designing Tools for Reflection: a concept-driven approach

We are surrounded by objects. We often use and interact with them to do our daily activities. They do not only support us and augment our abilities, but also, can be considered as companions of our thoughts. We think with objects, because they contain information about us: about our memories, experiences, emotions, and activities as Sherry Turkle highlights (2011). Furthermore, our everyday objects are increasingly computed, smart and connected to the Internet. They are able to collect data, elaborate and provide real-time feedbacks. These feedbacks cannot only support us to improve our activities, but also enables critical thinking and reflection on our actions. This resonates very well with what Donald Schön meant by having reflective conversation with materials at hand (1983;1996). He highlighted that materials –artifacts– of a situation talk back to designer, so they enable and support reflection in action of designing. So, how about if we consider that our daily objects can talk back and make us think on our actions in order to consider alternatives? This dissertation, is an attempt to consider this opportunity. The nature of this dissertation is mostly conceptual and its scope is defining the physical and behavioral characteristics of smart artifacts able to provoke thoughts and reflection in user leading to a conscious behavior change. I sought to use existing theories about reflective thinking in HCI and beyond, as valuable sources for developing design concept. I have been inspired by the Concept-Driven interaction design research (Stolterman and Wiberg 2011) and created and defined the whole structure of this dissertation based on this methodology, from the definition of the concept – Tool for Reflection – to the construction of a theoretical model from the design outcome –Make Me Think model. During this process, I used different methods such as conducting literature analysis, context analysis, survey, participatory session and prototyping. The sustainable urban mobility behaviors in the city of Turin (Italy) as the target behavior and home as the place for using Tool for Reflection have been chosen for this research. In particular, informed by architectural studies, I conceptualized In-Between Places as a category of places that connect home places to city places. I suggested to consider such areas as suitable places for evoking thoughts on urban mobility behaviors, in home. This dissertation provides a theoretical perspective with which to guide the design of smart objects that evoke reflection. It first provides a set of characteristics of a Tool for Reflection as a physical artifact. Then it provides a theoretical model, considering the relationship between a Tool for Reflection and a user. The key contributions include the design of the Sóle, a smart lamp, not only as an example of a Tool for Reflection with its theoretically pre-defined characteristics, but also as an instrument for iterating from design to the theory. The overall approach, the methodology and the findings should be of interest in particular to researchers working on design for reflection in the HCI. More broadly this dissertation can be of interest of researchers in the HCI, whose research is around designing artifacts, both as an ‘outcome’ and as an ‘instrument’ of the research process

Tracing the creation and evaluation of accessible Open Educational Resources through learning analytics

The adoption of Open Educational Resources (OER) has been continuously growing and with it the need to addressing the diversity of students’ learning needs. Because of that, OER should meet with characteristics such as the web accessibility and quality. Thus, teachers as the creators of OER need supporting tools and specialized competences. The main contribution of this thesis is a Learning Analytics Model to Trace the Creation and Evaluation of OER (LAMTCE) considering web accessibility and quality. LAMTCE also includes a user model of the teacher’s competences in the creation and evaluation of OER. Besides that, we developed ATCE, a learning analytics tool based on the LAMTCE model. Finally, it was carried out an evaluation conducted with teachers involving the use of the tool and we found that the tool really benefited teachers in the acquisition of their competences in creation and evaluation of accessible and quality OER.La adopción de Recursos Educativos Abiertos (REA) ha ido en aumento y con ello la necesidad de abordar la diversidad de necesidades de aprendizaje de los estudiantes. Por ello, los REA deben cumplir con características tales como la accesibilidad web y la calidad. Así, los profesores como los creadores de REA necesitan de herramientas de soporte y competencias especializadas. La principal contribución de la tesis es el modelo LAMTCE, un modelo de analíticas de aprendizaje para hacer seguimiento a la creación y evaluación de REA considerando la accesibilidad web y la calidad. LAMTCE también incluye un modelo de usuario de las competencias del profesor en creación y evaluación de REA. Además, se desarrolló ATCE, una herramienta de analíticas de aprendizaje que está basada en el modelo LAMTCE. Finalmente, se llevó a cabo un estudio con profesores involucrando el uso de la herramienta encontrando que ésta realmente benefició a los profesores en la adquisición de sus competencias en creación y evaluación de REA accesibles y de calidad

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