25 research outputs found
Data Analytics in Web-based Education in the Higher-education Classroom
No full textAttention span of students in a classroom is very short. To overcome this, different active learning methodologies have been used in the past. Active learning keeps the students busy and engaged throughout the lecture. It breaks the lecture into certain time intervals by intermixing breaks, demonstrations and questions after each interval. For using active learning, clickers and laptops are commonly used in higher education classroom. Most experiments in higher education classroom studying different characteristics of students like learning performance and attention, use clickers and laptop. But, most of these experiments are in a controlled setting, not scalable and compromise the privacy of students. We overcome these problems in an active learning setup in the higher education classroom where we use a web-mediated teaching tool called ASQ. ASQ is a web application that helps to give presentation in a classroom where the presenter has control over the flow of the presentation. ASQ also allows the presenter to interleave the presentation with questions, videos and other interactive JavaScript components. Anyone can anonymously join a presentation in ASQ using a web browser. ASQ tracks the activity of every student interaction by generating event logs each second. In the previous work using ASQ, it has been shown that these logs could be used to infer the attention level of students in the classroom. The goal of this thesis is to gather insights about the fine-grained study behaviour of students in a higher education classroom by analyzing these event logs.We investigate (i) the effect of lecture elements (like the difficulty, relative positioning and spacing of questions; and duration of discussion in the slides) on study behaviour (like attention level, performance and reaction time while answering questions) of students; (ii) the relationship that might exist between attention percentage of students and their participation in the in-class questions; (iii) if students are taking external help when answering questions during the lecture and the relationship that might exist between their tendency to take external help with the difficulty of questions. We conduct our study in a classroom of around 300 students, for 15 lectures in the Web and Database Technology course at TU Delft taught by 2 instructors. We find significant effect of (i) spacing of questions on reaction time and instructor on performance; (ii) length of discussion time associated with a slide on the attention level of students which agrees with past studies; (iii) relative positioning of questions on the performance of students. However, we do not find significant effect of difficulty of questions on performance and reaction time of students while answering these questions. We also find significant effect that students with more attention percentage participate more in the in-class questions. Finally, we find that students take external help while answering questions but the tendency to take external help does not depend on the difficulty of questions
Co-located Collaboration Analytics
No full textCollaboration is an important skill of the 21st century. It can take place in an online (or remote) setting or in a colocated (or face-to-face) setting. With the large scale adoption of sensor use, studies on co-located collaboration (CC) has gained momentum. CC takes place in physical spaces where the group members share each other's social and epistemic space. This involves subtle multimodal interactions such as gaze, gestures, speech, discourse which are complex in nature. The aim of this PhD is to detect these interactions and then use these insights to build an automated real-time feedback system to facilitate co-located collaboration.</p
Literature Review on Co-Located Collaboration Modeling Using Multimodal Learning Analytics—Can We Go the Whole Nine Yards?
No full textCollaboration is one of the important 21st-century skills. It can take place in remote or co-located settings. Co-located collaboration (CC) is a very complex process that involves subtle human interactions that can be described with indicators like eye gaze, speaking time, pitch, and social skills from different modalities. With the advent of sensors, multimodal learning analytics has gained momentum to detect CC quality. Indicators (or low-level events) can be used to detect CC quality with the help of measurable markers (i.e., indexes composed of one or more indicators) which give the high-level collaboration process definition. However, this understanding is incomplete without considering the scenarios (such as problem solving or meetings) of CC. The scenario of CC affects the set of indicators considered: For instance, in collaborative programming, grabbing the mouse from the partner is an indicator of collaboration; whereas in collaborative meetings, eye gaze, and audio level are indicators of collaboration. This can be a result of the differing goals and fundamental parameters (such as group behavior, interaction, or composition) in each scenario. In this article, we present our work on profiles of indicators on the basis of a scenario-driven prioritization, the parameters in different CC scenarios are mapped onto the indicators and the available indexes. This defines the conceptual model to support the design of a CC quality detection and prediction system.Web Information System
Group Coach for Co-located Collaboration
No full textCollaboration is an important 21st century skill; it can take place in a remote or co-located setting. Co-located collaboration (CC) gives rise to subtle human interactions that can be described with multimodal indicators like gaze, speech and social skills. In this demo paper, we first give a brief overview of related work that has identified indicators during CC. Then, we look briefly at the feedback mechanisms that have been designed based on these indicators to facilitate CC. Using these theoretical insights, we design a prototype to give automated real-time feedback to facilitate CC taking the help of the most abundant modality during CC i.e., audio cues.</p
Towards Collaborative Convergence: Quantifying Collaboration Quality with Automated Co-located Collaboration Analytics
No full textCollaboration is one of the four important 21st-century skills. With the pervasive use of sensors, interest on co-located collaboration (CC) has increased lately. Most related literature used the audio modality to detect indicators of collaboration (such as total speaking time and turn taking). CC takes place in physical spaces where group members share their social (i.e., non-verbal audio indicators like speaking time, gestures) and epistemic space (i.e., verbal audio indicators like the content of the conversation). Past literature has mostly focused on the social space to detect the quality of collaboration. In this study, we focus on both social and epistemic space with an emphasis on the epistemic space to understand different evolving collaboration patterns and collaborative convergence and quantify collaboration quality. We conduct field trials by collecting audio recordings in 14 different sessions in a university setting while the university staff and students collaborate over playing a board game to design a learning activity. This collaboration task consists of different phases with each collaborating member having been assigned a pre-fixed role. We analyze the collected group speech data to do role-based profiling and visualize it with the help of a dashboard
MULTIFOCUS - MULTImodal Learning Analytics FOr Co-located Collaboration Understanding and Support
No full text
Measuring Collaboration Quality Through Audio Data and Learning Analytics
No full textCollaboration is an important twenty-first-century skill. Collaboration quality detection can help to support collaboration. This chapter addresses the collaboration quality detection and measurement: (1) to define collaboration quality using audio data and unobtrusive learning analytics measures; (2) to explain the design of a sensor-based set up for automatic collaboration analytics; (3) to move toward quantifying the quality of collaboration by using this set up and show the analysis using meaningful visualizations. Furthermore, we address the challenges and issues at hand and how solutions can be built upon the work already done. To elaborate the different chapter’s objectives, we use the terminology of indicators (i.e., the events) and indexes (i.e., the process) to define the components to detect collaboration quality. In one study, during collaborative brainstorming, higher was the equality (i.e., the index) of total speaking time (i.e., the indicator), lower was the dominance of each group member (in terms of total speaking time), and better was the quality of collaboration. However, quality of collaboration is dependent on the context of collaboration and the actual content of the discussion. During collaboration content analysis has been mostly on the surface level by using certain representative keywords to model different topic clusters. Therefore, we develop a sensor-based setup for automatic collaboration analytics to understand collaboration quality holistically in a learning context. Here, our aim is to understand “how” group members speak (i.e., speaking time indicator) and “what’” (i.e., the content of the conversations) group members speak to move toward collaboration quality measurement
MULTIFOCUS - MULTImodal Learning Analytics FOr Co-located Collaboration Understanding and Support
No full text
