This paper presents results from our design and evaluation studies of the Learning Analytics Cockpit (LA Cockpit) for a quiz app, which aims to provide lecturers with important information about students’ knowledge levels. We define a LA Cockpit as a tool for instructors that enables them to steer students’ learning process by providing a LA Dashboard which visualizes students’ learning indicators and an intervention feature enabling instructors to give feedback based on students’ knowledge levels. To address the needs of lecturers we applied the Double Diamond (DD) design process model which consists of four stages: discover, define, develop & refine. Following the DD process, we first conducted a qualitative study by interviewing four lecturers and student teachers to discover their needs. Results from the interviews allowed us to define requirements of the lecturers. We used these results to develop the first version of the tool where we refined it through informal feedback by the interviewed teachers. In preparation for a larger effectiveness-study, we evaluated the LA Cockpit in terms of usefulness and usability in a preliminary study with 16 university lecturers. Results from this qualitative study indicate that the LA Cockpit can measure the students’ knowledge level and supports self-reflection for lecturers. Moreover, results show that the LA Cockpit enables lecturers to address knowledge gaps and provide interventions to students before the exams.

This paper introduces the Visual Inspection Tool (VIT) which supports researchers in the annotation of multimodal data as well as the processing and exploitation for learning purposes. While most of the existing Multimodal Learning Analytics (MMLA) solutions are tailor-made for specific learning tasks and sensors, the VIT addresses the data annotation for different types of learning tasks that can be captured with a customisable set of sensors in a flexible way. The VIT supports MMLA researchers in 1) triangulating multimodal data with video recordings; 2) segmenting the multimodal data into time-intervals and adding annotations to the time-intervals; 3) downloading the annotated dataset and using it for multimodal data analysis. The VIT is a crucial component that was so far missing in the available tools for MMLA research. By filling this gap we also identified an integrated workflow that characterises current MMLA research. We call this workflow the Multimodal Learning Analytics Pipeline, a toolkit for orchestration, the use and application of various MMLA tools.

Educational practitioners have stressed the relevance of providing learners with a set of 21th century skills that will allow them to face current life challenges. Among others this includes communication and social skills such as public speaking, argumentation, negotiation, etc. Besides the acquisition of knowledge and techniques, these skills have the special characteristic that their performance is usually conducted under emotionally charged and stressful situations. Recent advances in technology have allowed the creation of digital applications to support learners with the development of techniques for this type of skills. However, supporting learners on the preparation of a mindset that allows them to perform well under emotionally charged circumstances is an area that technology enhanced learning has practically overlooked. To examine this gap, we developed the Booth, an application designed to get learners into a powerful and resourceful emotional state. In this article we present a two-step user study. Results of the first evaluation show that the use of the Booth induced a positive emotional state on users. Results from the second step suggest that using the Booth helps learners to emotionally prepare for public speaking.

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback.