Abstract
Learning analytics (LA) identify patterns and insights to inform the teaching and learning process based on opportunities with new forms of digital data from students' creative and collaborative learning activities, for example in computer-supported collaborative learning (CSCL) and educational digital storytelling (EDS). Multimodal learning analytics (MMLA) offer opportunities to enhance students' outcomes in collaborative learning and can be used by both teachers and learners. Teachers can guide and regulate their students' activities through actionable knowledge; students can externalize and communicate their thoughts, feelings, and experiences, and be inspired by their teachers. Because of the discrepancies in the ways EDS can be structured and evaluated, this chapter proposes MMLA for EDS based on the hero's journey clock to inform, measure, and raise students' awareness, learning processes, active participation, and engagement in their collaborative learning.
TopIntroduction
The Society for Research in Learning Analytics (LA) (SoLAR, 2022; Tsai & Gasevic, 2017) defines LA as the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. LA sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, social computing). Significant effort has been invested in automatically mining digital traces of online group experiences, where logs can be captured in CSCL interfaces (Blikstein & Worsley, 2018). The result of the team-based collection and analysis of collaboration data is actionable knowledge that can be used for teams and learning regulation, guidance, and decision-making for both the teachers and the students. LA can serve a multitude of collaborative functions for monitoring, assessment, and recommendation (Chatti et al., 2012). Following Ochoa (2017), LA can be enriched with learning artefacts such as writing among others, as a predominant activity in learning, especially at early stages. In his PhD thesis, Shankar (2018) suggests that Multimodal Learning Analytics (MMLA) aims to support evidence-based educational practices by collecting, processing, analyzing and sense-making multimodal and multisystem evidence of learning. MMLA is the use of multiple data sources or modalities essential for measuring constructs (e.g., cognitive load, confusion, text, speech, handwriting, sketches, gesture, affective, or eye-gaze analysis) including the ones that cannot effectively be measured solely with the use of programming process data (Mangaroska et al., 2020). Such LA are important for learning to shed light, understand and gain insights to improve educational processes and outcomes (Şahin & Yurdugül, 2020).
In Computer-Supported Collaborative Learning (CSCL) these modalities suggest diverse data collection, such as text, audio, video, interaction logs, texts, interactive and transmedia content and storytelling. The MMLA goal is to enhance understanding of the learning processes for student engagement, and educational effectiveness with the diverse data collection and analysis. Traditional data sources such as student assessments, grades, and surveys, as well as more advanced sources such as text, audio, video, interaction logs or sensor data, are integrated and analysed. Another goal is to feed adaptive learning processes and learning regulation via assessment and feedback to enhance students’ learning experience. Lastly, educational institutions can use MMLA to improve curriculum design, teaching methods, and educational technology in collaborative learning for the co-construction of knowledge. Because the intermediate variables in these ecologies are invisible, the key is to find suitable models to describe and support collaborative interactions and their relationships with the conditions that facilitate them (Dillenbourg et al., 1996).
Key Terms in this Chapter
Educational Digital Storytelling: The combination of storytelling with new technologies and multimedia or/and transmedia such as text, images, audio, video, VR and other interactive elements and platforms to engage learners and deepen understanding by creating narrative stories to support and enhance the educational experience and build skills.
Socio-cultural learning: A theoretical framework which emphasizes the role of social interaction in the development of cognition. It supports that cognitive development is based on the negotiation of meaning that originates from individuals’ actual relationships.
Collaboration: A coordinated, synchronous activity because of a continued attempt to construct and maintain a shared conception of a problem.
Social Network Analysis (SNA): The mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities.
Social Computing: The incorporation of sociological understandings into interface design aiming at building systems that fit more easily into the ways we communicate and work.
Collaborative e-Learning Communities (CeLC): Social aggregations that emerge in online courses when enough people carry on progressive dialogues for the purpose of learning.
Computer-Supported Collaborative Learning (CSCL): The field concerned with how information and communication technology (ICT) might support learning in groups (co-located and distributed).
Human-Computer Interaction (HCI): The study, planning and design of what happens when humans interact with computers.
Multimodal Learning Analytics (MMLA): The use of multiple data sources or modalities essential for measuring constructs including the ones that cannot effectively be measured solely with the use of programming process data, and are important for learning to shed light, understand and gain insights to improve educational processes and outcomes.
Transmedia Storytelling: This (also known as transmedia narrative or multiplatform storytelling) is the technique of telling a single story or story experience across multiple platforms and formats using current digital technologies creating narrative synchronization with each other.
Online communities: Online social aggregations that emerge when enough people carry on those public discussions long enough to form relationships.
The Hero's Journey: A common narrative archetype (a monomyth) that involves a hero who embarks on an adventure, faces various trials, challenges, and learns a lesson, is victorious in a decisive crisis, undergoes a personal transformation with the newfound knowledge, and ultimately returns home changed or transformed with newfound wisdom or gifts.
SNA Centrality: Measures who is central (powerful) or isolated in networks.
Learning Analytics: The measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
Passive Participation: The activity of one of the “silent majority” in an electronic forum that involves posting occasionally or not at all but reading the group's postings.
Human-Computer Interaction Education (HCI-ED): The study, planning and design of what happens when learners as users interact with computers with the purpose of learning and act as both users and learners, having a dual persona.
Instructional Design: The systematic process of activities to solve an instructional problem with the aid of technologies anchored in Human-Computer Interaction.