The mediating effects of needs satisfaction on the relationships between prior knowledge and self-regulated learning through artificial intelligence chatbot

The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self-regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self-determination theory (SDT). In this study, we examine the mediating effects of needs satisfaction in SDT on the relationship between students' previous technical (AI) and disciplinary (English) knowledge and SRL, using an AI conversational chatbot. Data were collected from 323 9th Grade students through a questionnaire and a test. The students completed an AI basic unit and then learned English with a conversational chatbot for 5 days. Confidence intervals were calculated to investigate the mediating effects. We found that students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot, and that satisfying the need for autonomy and competence mediated the relationships between both knowledge (AI and English) and SRL, but relatedness did not. The self-directed nature of SRL requires heavy cognitive learning and satisfying the need for autonomy and competence may more effectively engage young children in this type of learning. The findings also revealed that current chatbot technologies may not benefit students with relatively lower levels of English proficiency. We suggest that teachers can use conversational chatbots for knowledge consolidation purposes, but not in SRL explorations.

Practitioner notes

What is already known about this topic

• Artificial intelligence (AI) technologies can potentially support students' self-regulated learning (SRL) of disciplinary knowledge through chatbots.
• Needs satisfaction in Self-determination theory (SDT) can explain the directive process required for SRL.
• Technical and disciplinary knowledge would affect SRL with technologies.

What this paper adds

• This study examines the mediating effects of needs satisfaction in SDT on the relationship between students' previous AI (technical) and English (disciplinary) knowledge and SRL, using an AI conversational chatbot.
• Students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot.
• Autonomy and competence were mediators, but relatedness was not.

Implications for practice and/or policy

• Teachers should use chatbots for knowledge consolidation rather than exploration.
• Teachers should support students' competence and autonomy, as these were found to be the factors that directly predicted SRL.
• School leaders and teacher educators should include the mediating effects of needs satisfaction in professional development programmes for digital education.

     

Imaginaries and problematisations: A heuristic lens in the age of artificial intelligence in education

This paper suggests that artificial intelligence in education (AIEd) can be fruitfully analysed as ‘policies frozen in silicon’. This means that they exist as both materialised and proposed problematisations (problem representations with corresponding solutions). As a theoretical and analytical response, this paper puts forward a heuristic lens that can provide insights into how AI technologies (or advocated AI technologies) function as proposed solutions to certain problematisations based on various imaginaries about how education and learning are best performed or supported. The combined reading of imaginaries and problematisations can thereby aid in our understanding of why and how visions of learning and education are framed in relation to AIEd developments. The overall ambition is to advance theoretical and analytical approaches towards an educational system which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology-augmented) education in society.

Practitioner notes

What is already known about this topic

• Artificial intelligence in education (AIEd) is repeatedly presented as a solution for a range of educational ‘problems’.
• This means that such ‘solutions’ must also frame certain aspects as ‘problems’.
• Such problems and ‘solutions’ (problematisations) also exist within certain imaginaries of the present times and of the future, where these problematisations are presented as particularly significant and acute, and promoting specific anticipations of learning and ideals of education.

What this paper adds

• An exposition of problematisations in educational settings.
• An exposition of educational imaginaries.
• A heuristic lens for understanding the ‘present’ and ‘future’ in a particular imaginary as entangled in, and dependent on, a certain ‘past’.

Implications for practice and/or policy

• The approach presented in this paper provides a heuristic lens for examining how AI technologies (or advocated AI technologies) function as proposed solutions to problematisations based on imaginaries about how education and learning are best performed or supported.
• This aids our understanding of how and why certain visions of learning and education are framed in relation to AIEd developments (real or imagined).
• It also advances theoretical and analytical approaches towards an educational system, which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology-augmented) education in society.

     

Table of Contents

• High‐achieving black collegians
  • by Sharon Fries‐Britt
  • How well do we understand the experiences of high‐achieving black students? The author says we have a lot to learn about their important roles in both our institutions and society at large.
• The search for a college commons
  • by Thomas Klein
  • How can we restructure our campuses to encourage the new kinds of learning we all know as so vital for the future of higher education? The author looks to the past for the answer.
• Listening to students: Richard J. Light talks to Charles C. Schroeder
  • What we can learn by simply giving students the chance to tell their stories and reflect on their experiences.

• DEPARTMENTS
• In practice—rainbow visibility: How one catholic university responded to intolerance
  • by Cheryl Getz and Evelyn A. Kirkley
  • When respect for the individual's dignity is threatened, education is often the best medicine.
• Campus commons—bulking up
  • by Lee Burdette Williams
  • How one university “mixes it with love and makes the world taste good.”
• Bottom line—imitate me
  • by William H. Willimon
  • Like it or not, example is the most potent instructor.

     

Human and artificial intelligence collaboration for socially shared regulation in learning

Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human-AI collaboration by introducing a novel trigger concept and a hybrid human-AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human-AI collaboration in facilitating effective SSRL in teaching and learning.

Practitioner notes

What is already known about this topic

• For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor.
• Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning.
• It is questionable whether traditional theories of how people learn are useful in the age of AI.

What this paper adds

• Introduces a trigger concept and a hybrid Human-AI Shared Regulation in Learning (HASRL) model to offer insights into how the human-AI collaboration could occur to operationalize SSRL research.
• Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning.
• Provides clear suggestions for future human-AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills.

Implications for practice and/or policy

• Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning.
• Researchers and practitioners could benefit from methodological development incorporating human-AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.

     

The pedagogy of multiliteracies as a code breaker: A suggestion for a transversal approach to computing education in basic education

While computing has been (re)introduced into the basic education curricula in various countries, its actual implementation appears to be inconsistent. There are schools in which computing education is commonplace, while the implementation seems to be lagging behind in others. There is emerging evidence that some teachers do not consider computing education relevant, meaningful and important and, thus, intentionally neglect its provision. This is problematic as understanding the principles of code and computing is crucial for agentic citizenship in the post-digital era. This paper argues that one main reason for these teachers' reluctance is the economy-driven discursive framing of computing education, which is in contrast with the socialization-oriented manner in which teachers approach their work. To contribute to resolving this issue, the present paper introduces a transversal approach to computing education. It conceptualizes code as a sociomaterial text with social and societal histories and consequences. Theoretically and conceptually, the approach draws on the pedagogy of multiliteracies. The leading idea is that digital technologies are examined with students from functional and critical dimensions and through micro and macro perspectives. The use of wearable sports technologies, such as activity wristbands, are used as practical examples to put the theoretical ideas into context.

Practitioner notes

What is already known about this topic

• Computing has been (re)introduced in the curricula of basic education in various countries.
• Some teachers are found to be reluctant to teach computing in basic education.

What this paper adds

• This paper introduces a transversal multiliteracies-based approach for computing education.

Implications for practice and policy

• Computing should be included in curricula and classrooms in a holistic manner that includes both functional and critical approaches to computing.

     

Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could be utilised to predict learning outcomes in collaborative learning.

Practitioner notes

What is already known about this topic

• Socially shared regulation has been recognised as an essential aspect of collaborative learning success.
• It is challenging to make the processes of learning regulation ‘visible’ to better understand and support student learning, especially in dynamic collaborative settings.
• Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.

What this paper adds

• Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.
• Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.
• Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.
• Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.

Implications for practice and/or policy

• Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.
• This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation.

     

Learning theories for artificial intelligence promoting learning processes

This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

Practitioner notes

What is already known about this topic

• Numerous learning theories exist with significant cross-over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning.
• Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified.
• Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices.

What this paper adds

• A three-level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education.
• A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels.
• The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels.
• Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity.

Implications for practice and policy

• Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems.
• Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

     

Assessing approaches to learning with nonparametric multidimensional scaling

This article reports on a trace-based assessment of approaches to learning used by middle school aged children who interacted with NASA Mars Mission science, technology, engineering and mathematics (STEM) games in Whyville, an online game environment with 8 million registered young learners. The learning objectives of two games included awareness and knowledge of NASA missions, developing knowledge and skills of measurement and scaling, applying measurement for planetary comparisons in the solar system. Trace data from 1361 interactions were analysed with nonparametric multidimensional scaling methods, which permitted visual examination and statistical validation, and provided an example and proof of concept for the multidimensional scaling approach to analysis of time-based behavioural data from a game or simulation. Differences in approach to learning were found illustrating the potential value of the methodology to curriculum and game-based learning designers as well as other creators of online STEM content for pre-college youth. The theoretical framework of the method and analysis makes use of the Epistemic Network Analysis toolkit as a post hoc data exploration platform, and the discussion centres on issues of semantic interpretation of interaction end-states and the application of evidence centred design in post hoc analysis.

Practitioner notes

What is already known about this topic

• Educational game play has been demonstrated to positively affect learning performance and learning persistence.
• Trace-based assessment from digital learning environments can focus on learning outcomes and processes drawn from user behaviour and contextual data.
• Existing approaches used in learning analytics do not (fully) meet criteria commonly used in psychometrics or for different forms of validity in assessment, even though some consider learning analytics a form of assessment in the broadest sense.
• Frameworks of knowledge representation in trace-based research often include concepts from cognitive psychology, education and cognitive science.

What this paper adds

• To assess skills-in-action, stronger connections of learning analytics with educational measurement can include parametric and nonparametric statistics integrated with theory-driven modelling and semantic network analysis approaches widening the basis for inferences, validity, meaning and understanding from digital traces.
• An expanded methodological foundation is offered for analysis in which nonparametric multidimensional scaling, multimodal analysis, epistemic network analysis and evidence-centred design are combined.

Implications for practice and policy

• The new foundations are suggested as a principled, theory-driven, embedded data collection and analysis framework that provides structure for reverse engineering of semantics as well as pre-planning frameworks that support creative freedom in the processes of creation of digital learning environments.

     

Contextualizing privacy with wearable data in higher education

This conceptual study uses dynamic systems theory (DST) and phenomenology as lenses to examine data privacy implications surrounding wearable devices that incorporate stakeholder, contextual and technical factors. Wearable devices can impact people's behaviour and sense of self, and DST and phenomenology provide complementary approaches for emphasizing the subjective experiences of individuals that occur with the use of wearable data. Privacy is approached through phenomenology as an individual's lived bodily experience and DST emphasizes the self-regulation and feedback loops of individuals and their uses of wearable data. The data collection, analysis and communication of wearable data to support learning systems alongside privacy implications for each are examined. The IoT, cloud computing, metadata and algorithms are discussed as they relate to wearable data, pointing out privacy risks and strategies to minimize harm.

Practitioner notes

What is already known about this topic

• Data privacy is a complex topic and is approached through different perspectives, influencing the degree of an individual's data autonomy.
• Wearable technology is increasing in the consumer market and offers great potential to learning environments.

What this paper adds

• Extends extant literature on dynamic systems theory and phenomenology, contributing these perspectives to educational research in the context of student data privacy and wearable technologies.
• Provides a framework to understand the complex and contingent ways that privacy can be understood in the collection, analysis, and communication of wearable data to support learning.

Implications for practice and/or policy

• Higher education faculty and educational policymakers should consider various interactions in systems and among systems of how wearable data collection may be analysed, communicated and stored, potentially exposing students to privacy harms.
• Multiple actors in learning systems must engage in continuous and evolving feedback loops around data security, consent, ownership and control to determine who has access to student data, how it is used and for what purposes.
• The EU's General Data Protection and Regulation offers one of the most comprehensive frameworks for higher education institutions and faculty around the world to follow for protecting student data privacy.

     

Algorithmic versus teacher-led sequencing in a digital serious game and the development of second language reading fluency and accuracy

Recent years have seen a surge of calls for personalization of education. Automatised adaptivity in serious games has been advocated as a potential instantiation of such calls. Yet little is known about the extent to which personalised learning through automatised adaptivity poses an advantage for language learning over generalised teacher-led sequencing in digital, game-based learning environments. The goal of this paper is to address this question by comparing the learning outcomes in reading accuracy and fluency of didactic sequences designed by EFL teachers or by an adaptive algorithm. A total of 67 participants completed several proficiency and reading skills pretest and posttest and used the iRead system for 6 months. Results showed that all learners made progress in reading skills, but no significant differences were found between the two sequences in relation to the development of reading skills. It was also shown that adaptivity works best if it leads to increase in the number of games per feature. Results are discussed in the context of previous findings, and the role of adaptivity and sequencing is critically assessed.

Practitioner notes

What is already known about this topic?

• Serious games have the potential to aid learning but empirical research is needed.
• Findings about the efficiency of serious games are mixed.
• Current and reviewed versions of the Simple View of Reading constitute a suitable framework to measure reading acquisition.

What this paper adds?

• It contributes to the growing corpus of research on digital serious games.
• It provides empirical evidence on the use of an adaptive system in formal education.
• Comparing a teacher-led sequence to an algorithmic adaptive sequence on the same digital serious game has never been done before.
• The paper shows the need to obtain both system-internal and system-external data in order to capture the impact of gameplay on the development of L2 reading skills.

Implications for practise and/or policy

• It sheds some light on how certain game designs may actually help practise with different degrees of intervention by teachers.
• It is interesting for teachers to use an adaptive sequence that they can check and intervene in if needed.

     

Students' privacy concerns in learning analytics: Model development

Understanding students' privacy concerns is an essential first step toward effective privacy-enhancing practices in learning analytics (LA). In this study, we develop and validate a model to explore the students' privacy concerns (SPICE) regarding LA practice in higher education. The SPICE model considers privacy concerns as a central construct between two antecedents—perceived privacy risk and perceived privacy control, and two outcomes—trusting beliefs and non-self-disclosure behaviours. To validate the model, data through an online survey were collected, and 132 students from three Swedish universities participated in the study. Partial least square results show that the model accounts for high variance in privacy concerns, trusting beliefs, and non-self-disclosure behaviours. They also illustrate that students' perceived privacy risk is a firm predictor of their privacy concerns. The students' privacy concerns and perceived privacy risk were found to affect their non-self-disclosure behaviours. Finally, the results show that the students' perceptions of privacy control and privacy risks determine their trusting beliefs. The study results contribute to understand the relationships between students' privacy concerns, trust and non-self-disclosure behaviours in the LA context. A set of relevant implications for LA systems' design and privacy-enhancing practices' development in higher education is offered.

Practitioner notes

What is already known about this topic

• Addressing students' privacy is critical for large-scale learning analytics (LA) implementation.
• Understanding students' privacy concerns is an essential first step to developing effective privacy-enhancing practices in LA.
• Several conceptual, not empirically validated frameworks focus on ethics and privacy in LA.

What this paper adds

• The paper offers a validated model to explore the nature of students' privacy concerns in LA in higher education.
• It provides an enhanced theoretical understanding of the relationship between privacy concerns, trust and self-disclosure behaviour in the LA context of higher education.
• It offers a set of relevant implications for LA researchers and practitioners.

Implications for practice and/or policy

• Students' perceptions of privacy risks and privacy control are antecedents of students' privacy concerns, trust in the higher education institution and the willingness to share personal information.
• Enhancing students' perceptions of privacy control and reducing perceptions of privacy risks are essential for LA adoption and success.
• Contextual factors that may influence students' privacy concerns should be considered.

     

The effects of augmented reality-supported instruction in tertiary-level medical education

A significant body of the literature has documented the potential of Augmented Reality (AR) in education, but little is known about the effects of AR-supported instruction in tertiary-level Medical Education (ME). This quasi-experimental study compares a traditional instructional approach with supplementary online lecture materials using digital handout notes with a control group (n = 30) and an educational AR application with an experimental group (n = 30) to investigate any possible added-value and gauge the impact of each approach on students' academic performance and training satisfaction. This study's findings indicate considerable differences in both academic performance and training satisfaction between the two groups. The participants in the experimental group performed significantly better than their counterparts, an outcome which is also reflected in their level of training satisfaction through interacting and viewing 3D multimedia content. This study contributes by providing guidelines on how an AR-supported intervention can be integrated into ME and provides empirical evidence on the benefits that such an approach can have on students' academic performance and knowledge acquisition.

Practitioner notes

What is already known about this topic

• Several studies have applied various Augmented Reality (AR) applications across different learning disciplines.
• The effects of AR on students' perceptions and achievements in higher education contexts is well-documented.
• Despite the increasing use of AR-instruction in Medical Education (ME), there has been no explicit focus on AR's effects on students' academic performance and satisfaction.

What this paper adds

• This quasi-experimental study compares the academic performance and training satisfaction of students in an experimental group (AR) and a control group (handout notes).
• This study provides instructional insights into, and recommendations that may help students achieve better academic performance in AR-supported ME courses.
• The experimental group reported greater training satisfaction than their counterparts.

Implications for practice and policy

• Students who followed the AR-supported instruction achieved better academic performance that those in the control group.
• AR-supported interventions encourage active learning and lead to significant performance improvement.
• The experimental group outperformed the control group in academic performance and training satisfaction measurements, despite the lower experimental group's lower pre-test performance scores.

     

Leveraging complexity frameworks to refine theories of engagement: Advancing self-regulated learning in the age of artificial intelligence

Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected from n = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control, n = 48), (2) perform poorly and received intervention (treatment, n = 95) and (3) perform well (not flagged, n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments.

Practitioner notes

What is already known about this topic

• Self-regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.
• SRL is a dynamic, temporal process that leads to purposeful student engagement.
• Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.

What this paper adds

• A Markov process for measuring dynamic SRL processes using log data.
• Evidence that dynamic, interaction-dominant aspects of SRL predict student achievement.
• Evidence that SRL processes can be meaningfully impacted through educational intervention.

Implications for theory and practice

• Complexity approaches inform theory and measurement of dynamic SRL processes.
• Static representations of dynamic SRL processes are promising learning analytics metrics.
• Engineered features of LMS usage are valuable contributions to AI models.

     

Analytics-supported reflective assessment for 6th graders' knowledge building and data science practices: An exploratory study

Preparing data-literate citizens and supporting future generations to effectively work with data is challenging. Engaging students in Knowledge Building (KB) may be a promising way to respond to this challenge because it requires students to reflect on and direct their inquiry with the support of data. Informed by previous studies, this research explored how an analytics-supported reflective assessment (AsRA)-enhanced KB design influenced 6th graders' KB and data science practices in a science education setting. One intact class with 56 students participated in this study. The analysis of students' Knowledge Forum discourse showed the positive influences of the AsRA-enhanced KB design on students' development of KB and data science practices. Further analysis of different-performing groups revealed that the AsRA-enhanced KB design was accessible to all performing groups. These findings have important implications for teachers and researchers who aim to develop students' KB and data science practices, and general high-level collaborative inquiry skills.

Practitioner notes

What is already known about this topic

• Data use becomes increasingly important in the K-12 educational context.
• Little is known about how to scaffold students to develop data science practices.
• Knowledge Building (KB) and learning analytics-supported reflective assessment (AsRA) show premises in developing these practices.

What this paper adds

• AsRA-enhanced KB can help students improve KB and data science practices over time.
• AsRA-enhanced KB design benefits students of different-performing groups.
• AsRA-enhanced KB is accessible to elementary school students in science education.

Implications for practice and/or policy

• Developing a collaborative and reflective culture helps students engage in collaborative inquiry.
• Pedagogical approaches and analytic tools can be developed to support students' data-driven decision-making in inquiry learning.

     

Table of Contents

• The Responsible Plagiarist—Understanding Students Who Misuse Sources
  • By Abigail Lipson and Sheila M. Reindl
  • Even students who are taking care not to plagiarize can misuse sources. The problem, argue the authors, isn't dishonesty or even carelessness, but students' understanding of what it means to participate in a community of scholars.
• Our Incoming Students—What Are They Like?
  • By Linda J. Sax
  • For thirty‐seven years the Cooperative Institutional Research Program has, among other things, conducted an annual survey of the students entering our colleges and universities. What do recent surveys tell us about the newest students?
• Studying How College Affects Students—A Personal History of the CIRP
  • By Alexander W. Astin
  • “Why in the world would anyone ever undertake such a project?” asks the author. He answers this and more in his autobiographical account of th origins and development of one of higher education's longest‐running research efforts.

• DEPARTMENTS
• Letters—How Many Latinos and Non‐Latino Whites Are There in the United States?
  • Gary Malaney clarifies; we respond.
• Word for Word—The Age of White Guilt and the Disappearance of the Black Individual
  • By Shelby Steele
  • In a recent article in Harper's magazine, the author of the book The Content of Our Character continues his examination of being black in today's world. Here is some of what he has to say.
• Campus Commons—Class Ring
  • By Scott C. Brown
  • All traditions had to get their start somewhere. But why this one? Why now?
• What They're Reading—Another Look at “Making the Most of College”
  • By Deborah J. Taub
  • For people who work with students, Richard J. Light's book may not be full of surprises, but, says our reviewer, there are many other good reasons to read it.

     

Technology addiction,abduction and adoption in higher education: Bird's eye view of the ICT4AD policy in Ghana 20 years on

Twenty years after its inauguration, the information communication and technology for accelerated development (ICT4AD) policy intended to transform Ghana into an information and technology-driven high-income economy through digital education has been unsuccessful. In this digital era, young adults' attachment to technological tools is anticipated to expedite technological adoption in the education sector. Still, there are less promising indicators of realizing this expectation because of situational factors that curtail technology usage and adoption in higher education (HE). It is estimated that the adoption of technology in HE will aid Ghana in using ICT as its engine of growth. This paper gauges the progress of the ICT4AD policy after two decades, presents an intricate account of why technology integration in HE in Ghana is still in its infancy and proposes interventions for sustaining and advancing the objectives of the ICT4AD policy. Drawing from an extensive review of literature on three conceptualized thematic themes relating to technology (ie, addiction, abduction and adoption), policymakers in education and stakeholders in HE will be able to identify their roles in guaranteeing the success of the promulgated ICT4AD policy. Viable areas of research are also discussed in the study.

Practitioner notes

What is already known about this topic?

• The promulgated information communication and technology for accelerated development (ICT4AD) policy of Ghana hopes to transform the country into a technology-driven economy.
• Technology integration in education and society is still in its infancy in Ghana in this information age.

What this paper adds?

• It gauges the progress of the ICT4AD policy and presents an intricate account of why technology integration in HE in Ghana is still in its infancy and proposes interventions for sustaining and advancing the objectives of the ICT4AD policy.
• It sounds the alarm that the ICT4AD policy is at its terminal stage and calls on policymakers in education to revisit and revise the policy.
• It identifies the main factors curtailing effective technology integration in Ghana.
• It suggests promising steps for Ghana to adopt technology as its engine of growth.

Implications of this study for practice and/or policy

• It provides information to education practitioners and relevant school stakeholders on how to effectively adopt technology to develop 21st-century skills among learners.
• It explores the potential channels for policymakers in education to revisit and reinvest in the ICT4AD policy for the successful attainment of the policy objectives.
• It calls on countries with similar contexts like Ghana to adopt a multifaceted approach to drive ICT initiatives.

     

Table of Contents

• Getting to the Top—What Role Do Elite Colleges Play?
  • By Karen D. Arnold
  • Most students think that a college education, no matter where, is a ticket to success. But how much does this success depend on which college students attend? The author argues that institutional prestige is a more important factor in creating pathways to leadership than we think.
• Reflection Across the Curriculum—Bringing Students' Experience to the Learning Process
  • By Catherine Marienau and Morry Fiddler
  • Meaningful learning is what we all want for our students—teaching students how to think and how to use their minds rather than simply imparting knowledge. This is a central challenge to anyone in higher education. But making it happen is not simple. Here is one approach that the authors have used with adult learners to encourage the kind of reflection and engagement that leads to deeper learning.
• Helping Students Find Their Place and Purpose—Tony Chambers Talks with Sharon Parks
  • As society becomes more and more complex our students' understanding about their place and purpose in life becomes increasingly unclear. Sharon Parks talks to Tony Chambers about how colleges and universities can become mentoring environments that help students find their way.

• DEPARTMENTS
• Letters to the Editor
  • More on students' drinking from Robert J. Chapman.
• In Practice—The Provost's Seminar: Building Community and Commitment
  • By Donald B. Kraybill
  • Building community within our institutions is always a challenge. One way, says the author, is at orientation, when new staff and faculty are coming on board. A former provost of Messiah College provides his approach and shows the broader positive results that emerged as a result.
• Campus Commons—Jumping the Gun
  • By Christine M. Cress
  • A lesson in keeping an open mind.
• What They're Reading—On Being a Generalist
  • By Dennis Pruitt
  • The power of reading widely and reading well.

     

The role of learning theory in multimodal learning analytics

This study presents the outcomes of a semi-systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory-driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control–value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory-driven MMLA research and how this acceleration can extend or even create new theoretical knowledge.

Practitioner notes

What is already known about this topic

• Multimodal learning analytics (MMLA) is an emerging field of research with inherent connections to advanced computational analyses of social phenomena.
• MMLA can help us monitor learning activity at the micro-level and model cognitive, affective and social factors associated with learning using data from both physical and digital spaces.
• MMLA provide new opportunities to support students' learning.

What this paper adds

• Some MMLA works use theory, but, overall, the role of theory is currently limited.
• The three theories dominating MMLA research are embodied cognition, control–value theory of achievement emotions and cognitive load theory.
• Most of the theory-driven MMLA papers use theory ‘as is’ and do not consider the analytical and synthetic role of theory or aim to contribute to it.

Implications for practice and/or policy

• If the ultimate goal of MMLA, and AI in Education in general, research is to understand and support human learning, these studies should be expected to align their findings (or not) with established relevant theories.
• MMLA research is mature enough to contribute to learning theory, and more research should aim to do so.
• MMLA researchers and practitioners, including technology designers, developers, educators and policy-makers, can use this review as an overview of the current state of theory-driven MMLA.

     

Comparing video and virtual reality as tools for fostering interest and self-efficacy in classroom management: Results of a pre-registered experiment

Video is a widely used medium in teacher training for situating student teachers in classroom scenarios. Although the emerging technology of virtual reality (VR) provides similar, and arguably more powerful, capabilities for immersing teachers in lifelike situations, its benefits and risks relative to video formats have received little attention in the research to date. The current study used a randomized pretest–posttest experimental design to examine the influence of a video- versus VR-based task on changing situational interest and self-efficacy in classroom management. Results from 49 student teachers revealed that the VR simulation led to higher increments in self-reported triggered interest and self-efficacy in classroom management, but also invoked higher extraneous cognitive load than a video viewing task. We discussed the implications of these results for pre-service teacher education and the design of VR environments for professional training purposes.

Practitioner notes

What is already known about this topic

• Video is a popular teacher training medium given its ability to display classroom situations.
• Virtual reality (VR) also immerses users in lifelike situations and has gained popularity in recent years.
• Situational interest and self-efficacy in classroom management is vital for student teachers' professional development.

What this paper adds

• VR outperforms video in promoting student teachers' triggered interest in classroom management.
• Student teachers felt more efficacious in classroom management after participating in VR.
• VR also invoked higher extraneous cognitive load than the video.

Implications for practice and/or policy

• VR provides an authentic teacher training environment for classroom management.
• The design of the VR training environment needs to ensure a low extraneous cognitive load.

     

The first steps for adapting an artificial intelligence emotion expression recognition software for emotional management in the educational context

To test the suitability of an automatic system for emotional management in the classroom following the control-value theory of achievement emotions (CVT) framework, the performance of an emotional expression recognition software of our creation is evaluated in an online synchronous context. Sixty students from the Faculty of Education at the University of Alicante participated in 16 educational activities recording close-ups of their faces and completing the AEQ emotional self-report, as well as detailed reports from the subsequent review of their videos. In addition, they completed the VCQ-36 test to measure their volitional competencies and relate their influence on their emotional response. The results indicate a high coherence between the emotional expressions detected by the automatic system and the detailed emotional self-reports, but insufficient precision to meet the CVT requirements. On the other hand, both the AEQ test results and the emotion expression recognition software suggest students' preference for participative activities as opposed to passive ones. Meanwhile, statistical analysis results indicate that volitional competencies seem to influence the emotional response of students in the educational context, although the AI system does not show sufficient sensitivity in this field. Implications and limitations of this study for future work are discussed.

Practitioner notes

What is already known about this topic

• Student motivation and involvement in the learning process are highly related to appropriate emotional regulation, which can be associated with particular educational activities, strategies and methodologies.
• Deep learning technology based on convolutional neural networks feeds automatic systems focused on facial expression recognition from image analysis.

What this paper adds

• There is high coherence between the emotional expressions detected by the AI system and the students' emotional self-reports, but the AI system provides just emotional valences, insufficient to meet the CVT framework.
• Both emotional self-reports and the emotion recognition software suggest students' preference for active educational activities as opposed to passive ones.
• Volitional competencies seem to influence the emotional response of students in the educational context.

Implications for practice and/or policy

• It is possible to use automatic systems to effectively monitor the emotional response of students in the learning process.
• Only if sensitivity improved, a real-time, easy-to-interpret emotional expression recognition software interface could be implemented to assist teachers with the emotional management of their classes within the CVT framework, maximizing their motivation and engagement.