It is reasonable to believe that students have preconceptions about a course and an education programme, standing on the threshold of entering. It is also likely that these preconceptions will influence the perception of a range of aspects of the course due to content, pedagogy, lecturers, and the level of satisfaction. These expectations were investigated at the time of course introductions at a Swedish university for three different courses and subjects in three different years. A mix of methods, qualitative and quantitative, via two open-ended surveys were employed: one focused on the expectations before the course introduction, the other on the reflections on the expectations after the course introduction. Overall, it can be concluded that the student groups are diverse regarding expectations of a course. Academic skills such as academic writing was highlighted as an expectation and seemed to overshadow other course objectives. Active verbs that govern different steps of learning were neglected and not noticed at all. The course introduction could be viewed as an underestimated aspect of developing a course design. However, as a teacher, it is imperative to raise one’s awareness of students’ expectations and preferences concerning the initial part of a curriculum design, ensuring its potential.

Föreliggande studie tar sin utgångspunkt i att studenter inför kursstart har förutfattade meningar om en kurs och ett utbildningsprogram. Sådana förutfattade meningar kan komma att påverka uppfattningen om en rad aspekter på kursen såsom innehåll, pedagogik, föreläsare och graden av tillfredsställelse. Studien belyser studentens förväntningar i samband med kursintroduktionen vid tre olika kurser samt ämnen under tre olika årskurser på ett svenskt universitet. Två enkätundersökningar genomfördes innefattande såväl kvalitativt som kvantitativt innehåll, varav den ena fokuserade på förväntningar inför kursintroduktionen, den andra på reflektioner kring förväntningarna efter kursintroduktionen. En övergripande slutsats är att studenterna har skilda och olika förväntningar på en kurs. Synen på akademiska färdigheter såsom akademiskt skrivande lyftes särskilt fram som en förväntan vilket tycktes överskugga andra kursmål. Aktiva verb som styr olika steg i inlärningen uppmärksammades inte alls. Kursintroduktionen kan ses som en underskattad del i att utveckla en kursdesign, som lärare är det dock centralt att öka medvetenheten om studenters förväntningar och preferenser särskilt när det gäller den inledande och introducerande kursdelen.

In the dynamic landscape of digital education, the Glocal Classroom (GC) stands out as a multifaceted smart learning environment. The integration of Multimodal Learning Analytics (MMLA) comes as an intriguing proposition, promising insights into learning dynamics and enhancing educational outcomes. Encountering numerous interdependent considerations involved in the design and integration of MMLA systems, the MMLA design framework (MDF) addresses this challenge, providing a systematic approach. MDF consists of a phased and iterative method for designing MMLA systems. In this study, we delve into the details of the fifth phase, focusing on the development phase. Our primary objective is to assess and refine the applicability of MDF, by taking the integration of MMLA in GC as a use case. We analyze GC's technological infrastructure, evaluating existing hardware, network capabilities, and potential challenges. The central emphasis is on the technical architecture, specifically the hardware components supporting MMLA. By focusing on the technical complexities, the study provides insights into challenges and opportunities associated with MMLA implementation. The outcomes will deepen our understanding of technology in education and refine the MDF model, making it more effective for designing MMLA systems.

This study details the technical design and evaluation of a Multimodal Learning Analytics (MMLA) system designed to enhance spoken language acquisition in language café settings. Utilizing the MMLA Model for Design and Analysis (MAMDA) framework, we outline the development of a prototype system that integrates AI voice assistance with the collection and analysis of multimodal data, including audio and video. We provide details about the specific technologies and algorithms employed, such as the Arduino Nicla Vision board for participant tracking and deep learning techniques for audio analysis. The implementation of the prototype for real-world language café sessions highlights its potential for providing valuable insights into learning patterns and interaction dynamics. We discuss the system's performance and limitations, paving the way for future refinements and broader applications in education.

This study investigates the application of Multimodal Learning Analytics (MMLA) in language practice, specifically within the authentic and dynamic environment of language café settings. The MMLA Model for Design and Analysis (MAMDA), a design science approach, is utilized to systematically explore the requirements for designing the MMLA system. We identify and map three elements: 1) Learning indicators, referring to spoken language learning signs, such as tone, amount and frequency of speech, and pronunciation. 2) Respective modalities and sensors, referring to the format of data to be collected and 3) Analytics models, including NLP models, that can be employed to identify and process the modalities. We propose a conceptual system that utilizes AI voice assistant while simultaneously collecting audio data for MMLA to enhance language learning experiences. The system is meant for providing insights into learning patterns, participant engagement, and the overall effectiveness of language practice strategies. While presenting a novel system showcasing the use of AI and data analytics in a unique educational setting, the study's central focus is to test and critically reflect on MAMDA as a framework for designing and analyzing MMLA systems.

Designing MMLA systems is a complex task requiring a wide range of considerations. In this paper, we identify key considerations that are essential for designing MMLA systems. These considerations include data management, human factors, sensors and modalities, learning scenarios, privacy and ethics, interpretation and feedback, and data collection. The implications of these considerations are twofold: 1) The need for flexibility in MMLA systems to adapt to different learning contexts and scales, and 2) The need for a researcher-centered approach to designing MMLA systems. Unfortunately, the sheer number of considerations can lead to a state of "analysis paralysis," where deciding where to begin and how to proceed becomes overwhelming. This synthesis paper asks researchers to rethink the design of MMLA systems and aims to provide guidance for developers and practitioners in the field of MMLA.

The Scandinavian countries are often seen as a unity. However, during the COVID-19 pandemic striking differences on how the countries approached the crisis became evident. This quantitative-ethnographic (QE) study aimed to understand political and cultural similarities and differences between the three Scandinavian countries – Denmark, Norway and Sweden – through their crisis communications during the COVID-19 pandemic. Specifically, we focused on how the health authorities of the three countries, in their press releases, treated information about COVID-19 and acted in four fields: reorganization of population behavior, containment of viral transmission, preparation of health systems, and management of socioeconomic impacts. As a methodology, the QE tools nCoder and ENA were applied, respectively: to code the press releases and to correlate the treatment of information with the four fields of action.

Multimodal Learning Analytics (MMLA) provides opportunities for understanding and supporting collaborative problem-solving. However, the implementation of MMLA systems is challenging due to the lack of scalable technologies and limited solutions for collecting data from group work. This paper proposes the Multimodal Box (MBOX), an IoT-based system for MMLA, allowing the collection and processing of multimodal data from collaborative learning tasks. MBOX investigates the development and design for an IoT focusing on small group work in real-world settings. Moreover, MBOX promotes adaptation to different learning environments and enables a better scaling of computational resources used within the learning context.

Learning is a complex process that is associated with many aspects of interactionand cognition (e.g., hard mental operations, cognitive friction etc.) and that can take acrossdiverse contexts (online, classrooms, labs, maker spaces, etc.). The complexity of this processand its environments means that it is likely that no single data modality can paint a completepicture of the learning experience, requiring multiple data streams from different sourcesand times to complement each other. The need to understand and improve learning thatoccurs in ever increasingly open, distributed, subject-specific and ubiquitous scenarios,require the development of multimodal and multisystem learning analytics. Following thetradition of CrossMMLA workshop series, the proposed workshop aims to serve as a place tolearn about the latest advances in the design, implementation and adoption of systems thattake into account the different modalities of human learning and the diverse settings inwhich it takes place. Apart from the necessary interchange of ideas, it is also the objective ofthis workshop to develop critical discussion, debate and co-development of ideas foradvancing the state-of-the-art in CrossMMLA.

Today’s digital world requires students to gain skills in collaborative problem solving and digital literacy. One approach is to teach people how to design computational artefacts that require both electronics and programming. Physical computing platforms offer an endless amount of possible opportunities for people to design and develop technological artefacts. However, many times students are overwhelmed when trying to learn both software and hardware simultaneously. The students struggle to be innovative and creative in their projects. Also, they focus on mastering the tool and following instructions for existing projects rather than being able to creatively explore the tool and understand the process of designing and developing new artefacts. For that reason, we aimed to answer the question: What type of tools and activities can be developed to support university students creative exploration of physical computing? Programming and electronics are fundamental design disciplines in today's digital world, and therefore they should be taught through design activities rather than limiting education to textbook readings and laboratory exercises. We introduce our process of designing activities combined with a supportive tool to ease these challenges. The activities and tools were developed iteratively in three phases with a series of workshops with 126 students and teachers. The tool consists of a set of paper cards that provide necessary details (hints) about the electronics and software and help provide structure for the students to conceptualise how their artefact interacts. We additionally, introduced a learning Jigsaw pattern (orchestration script) for the later intervention that enabled individual students in the groups to focus on design, hardware, or software. For evaluation, we used the Creativity Support Index (CSI), which is a psychometric survey designed to assess the support of the creative process. The instrument investigates collaboration, efforts worth the result, exploration, immersion, enjoyment, and expressiveness. The results between the phases showed improvement with the use of the refined versions of the cards and orchestration of the learning activity. This study has demonstrated that design activities can provide a more accessible approach for the introduction of physical computing to students from various majors. Moreover, learning physical computing through design activities allows the learner to develop computational and design thinking skills for collaboratively solving problems.