Empowering educators to be AI-ready

In this paper, we present the concept of AI Readiness, along with a framework for developing AI Readiness training. ‘AI Readiness’ can be framed as a contextualised way of helping people to understand AI, in particular, data-driven AI. The nature of AI Readiness training is not the same as merely learning about AI. Rather, AI Readiness recognises the diversity of the professions, workplaces and sectors for whom AI has a potential impact. For example, AI Readiness for lawyers may be based on the same principles as AI Readiness for Educators. However, the details will be contextualised differently. AI Readiness recognises that such contextualisation is not an option: it is essential due to the multiple intricacies, sensitivities and variations between different sectors and their settings, which all impact the application of AI. To embrace such contextualisation, AI Readiness needs to be an active, participatory training process and aims to empower people to be more able to leverage AI to meet their needs. The text that follows focuses on AI Readiness within the Education and Training sector and starts with a discussion of the current state of AI within education and training, and the need for AI Readiness. We then problematize the concept of AI Readiness, why AI Readiness is needed, and what it means. We expand upon the nature of AI Readiness through a discussion of the difference between human and Artificial Intelligence, before presenting a 7-step framework for helping people to become AI Ready. Finally, we use an example of AI Readiness in action within Higher Education to exemplify AI Readiness

How interactive is a semantic network ? Concept maps and discourse in knowledge communities

Computer-mediated learning needs to be social too. Interactivity is a central construct for collaborative knowledge construction in online communities. We present an operationalized framework for measuring interactivity in online discussions, based on our view of interactivity as a socio-constructivist process. We hypothesize that the traditional design for online discussion platforms, with linear, chronologically threaded forums and bulletin boards, would result in less interactive behavioral patterns. We propose a semantic network topology to online discussions, which in turn reflects a social constructivist process. To that end, we developed Ligilo, an online discussion platform. Here, each discussion contribution and content item is expressed as a node in a semantic network of posts. We describe a field study comparing interactivity using threaded-based discussion and Ligilo's semantic, networked based discussion. Initial results indicate higher interactivity in content creation patterns, suggesting learning, motivation and sustainability for discussion and community

Investigating Collaboration as a Process with Theory-driven Learning Analytics

Although collaboration is considered a key 21st-century skill, oftentimes it is only assessed through the outcome measures of individual learners. In this paper, we draw upon collaborative cognitive load theory (CCLT) to explain the process of collaboration in learning from the point of view of the collective, rather than an individual learner. Using CCLT we suggest a new method of measuring the process of collaboration regardless of its outcome measures. Our approach — Collaborative Learning as a Process (CLaP) — uses social network analysis to evaluate the balance between interactivity gains and coordination costs of learner communities. Here, we demonstrate the approach using real-world data derived from the digital tracks of two online discussion communities. We argue that our conceptual approach can enable instructors and learners to unlock the black box of collaboration in learning. It has the potential to support the development of learner skills that go beyond cognition. We conclude the paper with the results of our investigation of the value of the approach to the online module instructor

Multiscale Analyses of Mammal Species Composition – Environment Relationship in the Contiguous USA

Relationships between species composition and its environmental determinants are a basic objective of ecology. Such relationships are scale dependent, and predictors of species composition typically include variables such as climate, topographic, historical legacies, land uses, human population levels, and random processes. Our objective was to quantify the effect of environmental determinants on U.S. mammal composition at various spatial scales. We found that climate was the predominant factor affecting species composition, and its relative impact increased in correlation with the increase of the spatial scale. Another factor affecting species composition is land-use–land-cover. Our findings showed that its impact decreased as the spatial scale increased. We provide quantitative indication of highly significant effect of climate and land-use–land-cover variables on mammal composition at multiple scales

Impact of an Artificial Intelligence Research Frame on the Perceived Credibility of Educational Research Evidence

Artificial Intelligence (AI) is attracting a great deal of attention and it is important to investigate the public perceptions of AI and their impact on the perceived credibility of research evidence. In the literature, there is evidence that people overweight research evidence when framed in neuroscience findings. In this paper, we present the findings of the first investigation of the impact of an AI frame on the perceived credibility of educational research evidence. In an experimental study, we allocated 605 participants including educators to one of three conditions in which the same educational research evidence was framed within one of: AI, neuroscience, or educational psychology. The results demonstrate that when educational research evidence is framed within AI research, it is considered as less credible in comparison to when it is framed instead within neuroscience or educational psychology. The effect is still evident when the subjects' familiarity with the framing discipline is controlled for. Furthermore, our results indicate that the general public perceives AI to be: less helpful in assisting us to understand how children learn, lacking in adherence to scientific methods, and to be less prestigious compared to neuroscience and educational psychology. Considering the increased use of AI technologies in Educational settings, we argue that there should be significant attempts to recover the public image of AI being less scientifically robust and less prestigious than educational psychology and neuroscience. We conclude the article suggesting that AI in Education community should attempt to be more actively engaged with key stakeholders of AI and Education to help mitigate such effects

Creativity is Connecting Things: The Role of Network Topology in Fostering Collective Creativity in Multi-Participant Asynchronous Online Discussions.

Creativity derives from the ability to form new meaningful combinations out of available resources. Collective creativity is the product of a collaborative process, consisting of multiple interactions between group members and the shared content, which lead to the emergence of novel shared meanings. This exploratory research addresses the expression of collective creativity in multi-participant asynchronous online discussions, by proposing interactivity and emergence as key features of the collaborative creative process. The ability to connect posts in a non-sequential manner ("cross-linking") is suggested as the basis for the formation of emergent community-structures within the content, which reflect collectively constructed novel combinations. Initial indications for this process are presented by applying a combination of network analysis and qualitative inquiry to data from a multiparticipant virtual discussion, held as part of an online academic course. A methodology for extracting emergent themes is described

How ‘Networked’ are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the ‘Networkedness’ of Collaboratively Constructed Content

With the growing role of online multi-participant collaborations in shaping the academic, professional, and civic spheres, incorporating collaborative online practices in educational settings has become imperative. As more educators include such practices in their curricula, they are faced with new challenges. Assessment of collaborations, especially in larger groups, is particularly challenging. Assessing the quality of the collaborative “thought process” and its product is essential for both pedagogical and evaluative purposes. While traditional quantitative quality measures were designed for individual work or the aggregated work of individuals, capturing the complexity and the integrative nature of high-quality collaborative learning requires novel methodologies. Network analysis provides methods and tools that can identify, describe, and quantify non-linear and complex phenomena. This paper applies network analysis to the content created by students through large-scale online collaborative concept-mapping and explores how these can be applied for the assessment of the quality of a collective product. Quantitative network structure measures are introduced for this purpose. The application and the affordances of these metrics are demonstrated on data from six large-group online collaborative discussions from academic settings. The metrics presented here address the organization and the integration of the content and enable a comparison of collaborative discussions

Topological Parameters of Networked Learning

This paper proposes a methodological linking of learning theories and social learning analytics, applicable in real-life settings. We view online discussions in learning communities as networks in which human agents and posts of content are the nodes, while the interactions between nodes are represented as edges. The collaborative learning process is thus viewed as the construction and growth of a network involving various types of interactions among learners and content items. We contend that online learning assessment should be grounded by a theoretical framework based on quantitative analysis of the collaborative learning process as reflected by learning communities' online discussions. This paper demonstrates our proposed framework through a case study of a single community's online discussion, which took place throughout one academic semester. We use social network analysis (SNA) and other log-based techniques to determine topological parameters or network characteristics, assessing both collective and individual learning process which took place as reflected by the community online discussion

The Relationship Between Offline Social Capital and Online Learning Interactions

This article examines the interplay between offline social capital and online interactivity in higher education's online learning discussions. In a field study, we examine networks of interactions extracted from the online discussions and offline acquittance questionnaire of four classes. Two classes belong to a traditional brick-and-mortar university, where an offline acquaintance is a common resource, and two classes belong to a distance-learning university with a loose offline acquaintance. We analyzed the offline and online networks of interactions at the individual, dyadic, and community levels. We found that there is a positive association between offline social capital and online learning interactions across all classes at the individual and dyadic levels. Using network analysis, we found evidence for a substitutional relationship between the offline and online networks at the community level, thus suggesting that online interactions may be encouraged as a complementing dimension of offline social capital

Keeping the Parents outside the School Gate—A Critical Review

The existing evidence shows that parental engagement is one of the most effective educational interventions. Most parents, carers, and teachers are aware of that and wish to engage with their children’s education. However, most parents are still only peripherally involved through parent–teacher evenings, school activities, or by helping their children keep up with their homework. In this review paper, we summarize the evidence about the impact of parental engagement, as opposed to involvement, on the learning of children. Via that, we critically look at the design choice of most western mainstream public education systems to distance parents from their children’s education, which, as the review results indicate, can be detrimental to children’s learning. Based on these results, we reframe parental engagement in the light of two global shifts: (1) the implications of the school closures during the COVID-19 pandemic for the role of parents in their children’s learning; and (2) the increased use of educational technologies for learning, and specifically, the rise of artificial intelligence (AI) technologies. We conclude by calling for a renewed conversation about parents’ and families’ roles in their children’s learning and their interface with schools and teachers