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1.
Gerald Knezek David Gibson Rhonda Christensen Ottavia Trevisan Morgan Carter 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):126-141
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.
- 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.
- 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.
2.
Qinyi Liu Mohammad Khalil 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(6):1715-1747
The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.
Practitioner notes
What is already known about this topic- Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
- Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.
- Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
- Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
- Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.
- Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
- Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
- Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.
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4.
Andy Nguyen Sanna Järvelä Carolyn Rosé Hanna Järvenoja Jonna Malmberg 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):293-312
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.
- 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.
- 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.
5.
David Gibson Vitomir Kovanovic Dirk Ifenthaler Sara Dexter Shihui Feng 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1125-1146
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.
- 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.
- 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.
6.
Judith Stanja Wolfgang Gritz Johannes Krugel Anett Hoppe Sarah Dannemann 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):58-75
Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for meaningful learning across disciplines, teachers are required to assess their students' conceptions. The focus of this article lies on the discussion of learning analytics for supporting the assessment of students' conceptions in class. The existing and potential contributions of learning analytics are discussed related to the named formative assessment framework in order to enhance the teachers' options to consider individual students' conceptions. We refer to findings from biology and computer science education on existing assessment tools and identify limitations and potentials with respect to the assessment of students' conceptions.
Practitioner notes
What is already known about this topic- Students' conceptions are considered to be important for learning processes, but interpreting evidence for learning with respect to students' conceptions is challenging for teachers.
- Assessment tools have been developed in different educational domains for teaching practice.
- Techniques from artificial intelligence and machine learning have been applied for automated assessment of specific aspects of learning.
- Findings on existing assessment tools from two educational domains are summarised and limitations with respect to assessment of students' conceptions are identified.
- Relevent data that needs to be analysed for insights into students' conceptions is identified from an educational perspective.
- Potential contributions of learning analytics to support the challenging task to elicit students' conceptions are discussed.
- Learning analytics can enhance the eliciting of students' conceptions.
- Based on the analysis of existing works, further exploration and developments of analysis techniques for unstructured text and multimodal data are desirable to support the eliciting of students' conceptions.
7.
Yoon Jeon Kim Mariah A. Knowles Jennifer Scianna Grace Lin José A. Ruipérez-Valiente 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):355-372
Game-based assessment (GBA), a specific application of games for learning, has been recognized as an alternative form of assessment. While there is a substantive body of literature that supports the educational benefits of GBA, limited work investigates the validity and generalizability of such systems. In this paper, we describe applications of learning analytics methods to provide evidence for psychometric qualities of a digital GBA called Shadowspect, particularly to what extent Shadowspect is a robust assessment tool for middle school students' spatial reasoning skills. Our findings indicate that Shadowspect is a valid assessment for spatial reasoning skills, and it has comparable precision for both male and female students. In addition, students' enjoyment of the game is positively related to their overall competency as measured by the game regardless of the level of their existing spatial reasoning skills.
Practitioner notes
What is already known about this topic:- Digital games can be a powerful context to support and assess student learning.
- Games as assessments need to meet certain psychometric qualities such as validity and generalizability.
- Learning analytics provide useful ways to establish assessment models for educational games, as well as to investigate their psychometric qualities.
- How a digital game can be coupled with learning analytics practices to assess spatial reasoning skills.
- How to evaluate psychometric qualities of game-based assessment using learning analytics techniques.
- Investigation of validity and generalizability of game-based assessment for spatial reasoning skills and the interplay of the game-based assessment with enjoyment.
- Game-based assessments that incorporate learning analytics can be used as an alternative to pencil-and-paper tests to measure cognitive skills such as spatial reasoning.
- More training and assessment of spatial reasoning embedded in games can motivate students who might not be on the STEM tracks, thus broadening participation in STEM.
- Game-based learning and assessment researchers should consider possible factors that affect how certain populations of students enjoy educational games, so it does not further marginalize specific student populations.
8.
Kirsty Kitto Ben Hicks Simon Buckingham Shum 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1095-1124
An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.
Practitioner notes
What is already known about this topic
- ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
- Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
- Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.
What this paper adds
- An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
- A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
- An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.
Implications for practice and/or policy
- Causal models can help us to explicitly specify educational theories in a testable format.
- It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
- Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.
9.
Jonathan C. Hilpert Jeffrey A. Greene Matthew Bernacki 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1204-1221
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.
- 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.
- 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.
10.
José Antonio Rodríguez-Martínez José Antonio González-Calero Javier del Olmo-Muñoz David Arnau Sergio Tirado-Olivares 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):76-97
This study analyses the potential of a learning analytics (LA) based formative assessment to construct personalised teaching sequences in Mathematics for 5th-grade primary school students. A total of 127 students from Spanish public schools participated in the study. The quasi-experimental study was conducted over the course of six sessions, in which both control and experimental groups participated in a teaching sequence based on mathematical problems. In each session, both groups used audience response systems to record their responses to mathematical tasks about fractions. After each session, students from the control group were given generic homework on fractions—the same activities for all the participants—while students from the experimental group were given a personalised set of activities. The provision of personalised homework was based on the students' errors detected from the use of the LA-based formative assessment. After the intervention, the results indicate a higher student level of understanding of the concept of fractions in the experimental group compared to the control group. Related to motivational dimensions, results indicated that instruction using audience response systems has a positive effect compared to regular mathematics classes.
Practitioner notes
What is already known about this topic- Developing an understanding of fractions is one of the most challenging concepts in elementary mathematics and a solid predictor of future achievements in mathematics.
- Learning analytics (LA) has the potential to provide quality, functional data for assessing and supporting learners' difficulties.
- Audience response systems (ARS) are one of the most practical ways to collect data for LA in classroom environments.
- There is a scarcity of field research implementations on LA mediated by ARS in real contexts of elementary school classrooms.
- Empirical evidence about how LA-based formative assessments can enable personalised homework to support student understanding of fractions.
- Personalised homework based on an LA-based formative assessment improves the students' comprehension of fractions.
- Using ARS for the teaching of fractions has a positive effect in terms of student motivation.
- Teachers should be given LA/ARS tools that allow them to quickly provide students with personalised mathematical instruction.
- Researchers should continue exploring these potentially beneficial educational implementations in other areas.
11.
Chantal Mutimukwe Olga Viberg Lena-Maria Oberg Teresa Cerratto-Pargman 《British journal of educational technology : journal of the Council for Educational Technology》2022,53(4):932-951
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.
- 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.
- 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.
12.
Colin Conrad Qi Deng Isabelle Caron Oksana Shkurska Paulette Skerrett Binod Sundararajan 《British journal of educational technology : journal of the Council for Educational Technology》2022,53(3):534-557
Practitioner notes
What is already known about this topic- University transitions to online learning during the Covid-19 pandemic were undertaken by faculty and students who had little online learning experience.
- The transition to online learning was often described as having a negative influence on students' learning experience and mental health.
- Varieties of cognitive load are known predictors of effective online learning experiences and satisfaction.
- Information overload and perceptions of technical abilities are demonstrated to predict students' difficulty and satisfaction with online learning.
- Students express negative attitudes towards factors that influence information overload, technical factors, and asynchronous course formats.
- Communication quantity was not found to be a significant factor in predicting either perceived difficulty or negative attitudes.
- We identify ways that educators in higher education can improve their online offerings and implementations during future disruptions.
- We offer insights into student experience concerning online learning environments during an abrupt transition.
- We identify design factors that contribute to effective online delivery, educators in higher education can improve students' learning experiences during difficult periods and abrupt transitions to online learning.
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14.
Sanna Järvelä Andy Nguyen Allyson Hadwin 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1057-1076
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.
- 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.
- 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.
15.
Oscar Blessed Deho Chen Zhan Jiuyong Li Jixue Liu Lin Liu Thuc Duy Le 《British journal of educational technology : journal of the Council for Educational Technology》2022,53(4):822-843
Practitioner notes
What is already known about this topic- LA is increasingly being used to leverage actionable insights about students and drive student success.
- LA models have been found to make discriminatory decisions against certain student demographic subgroups—therefore, raising ethical concerns.
- Fairness in education is nascent. Only a few works have examined fairness in LA and consequently followed up with ensuring fair LA models.
- A juxtaposition of unfairness mitigation algorithms across the entire LA pipeline showing how they compare and how each of them contributes to fair LA.
- Ensuring ethical LA does not always lead to a dip in performance. Sometimes, it actually improves performance as well.
- Fairness in LA has only focused on some form of outcome equality, however equality of outcome may be possible only when the playing field is levelled.
- Based on desired notion of fairness and which segment of the LA pipeline is accessible, a fairness-minded decision maker may be able to decide which algorithm to use in order to achieve their ethical goals.
- LA practitioners can carefully aim for more ethical LA models without trading significant utility by selecting algorithms that find the right balance between the two objectives.
- Fairness enhancing technologies should be cautiously used as guides—not final decision makers. Human domain experts must be kept in the loop to handle the dynamics of transcending fair LA beyond equality to equitable LA.
16.
Karen D. Wang Jade Maï Cock Tanja Käser Engin Bumbacher 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):192-221
Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry and problem solving. How to parse and analyse the log data to reveal evidence of multifaceted constructs like inquiry and problem solving holds the key to making interactive learning environments useful for assessing students' higher-order competencies. In this paper, we present a systematic review of studies that used log data generated in OELEs to describe, model and assess scientific inquiry and problem solving. We identify and analyse 70 conference proceedings and journal papers published between 2012 and 2021. Our results reveal large variations in OELE and task characteristics, approaches used to extract features from log data and interpretation models used to link features to target constructs. While the educational data mining and learning analytics communities have made progress in leveraging log data to model inquiry and problem solving, multiple barriers still exist to hamper the production of representative, reproducible and generalizable results. Based on the trends identified, we lay out a set of recommendations pertaining to key aspects of the workflow that we believe will help the field develop more systematic approaches to designing and using OELEs for studying how students engage in inquiry and problem-solving practices.
Practitioner notes
What is already known about this topic- Research has shown that technology-based, open-ended learning environments (OELEs) that collect users' interaction data are potentially useful tools for engaging students in practice-based STEM learning.
- More work is needed to identify generalizable principles of how to design OELE tasks to support student learning and how to analyse the log data to assess student performance.
- We identified multiple barriers to the production of sufficiently generalizable and robust results to inform practice, with respect to: (1) the design characteristics of the OELE-based tasks, (2) the target competencies measured, (3) the approaches and techniques used to extract features from log files and (4) the models used to link features to the competencies.
- Based on this analysis, we can provide a series of specific recommendations to inform future research and facilitate the generalizability and interpretability of results:
- Making the data available in open-access repositories, similar to the PISA tasks, for easy access and sharing.
- Defining target practices more precisely to better align task design with target practices and to facilitate between-study comparisons.
- More systematic evaluation of OELE and task designs to improve the psychometric properties of OELE-based measurement tasks and analysis processes.
- Focusing more on internal and external validation of both feature generation processes and statistical models, for example with data from different samples or by systematically varying the analysis methods.
- Using the framework of evidence-centered assessment design, we have identified relevant criteria for organizing and evaluating the diverse body of empirical studies on the topic and that policy makers and practitioners can use for their own further examinations.
- This paper identifies promising research and development areas on the measurement and assessment of higher-order constructs with process data from OELE-based tasks that government agencies and foundations can support.
- Researchers, technologists and assessment designers might find useful the insights and recommendations for how OELEs can enhance science assessment through thoughtful integration of learning theories, task design and data mining techniques.
17.
Qi Xia Thomas K. F. Chiu Ching Sing Chai Kui Xie 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(4):967-986
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.
- 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.
- 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.
18.
Bethany Huntington James Goulding Nicola J. Pitchford 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1273-1291
Interactive apps are commonly used to support the acquisition of foundational skills. Yet little is known about how pedagogical features of such apps affect learning outcomes, attainment and motivation—particularly when deployed in lower-income contexts, where educational gains are most needed. In this study, we analyse which app features are most effective in supporting the acquisition of foundational literacy and numeracy skills. We compare five apps developed for the Global Learning XPRIZE and deployed to 2041 out-of-school children in 172 remote Tanzanian villages. A total of 41 non-expert participants each provided 165 comparative judgements of the five apps from the competition, across 15 pedagogical features. Analysis and modelling of these 6765 comparisons indicate that the apps created by the joint winners of the XPRIZE, who produced the greatest learning outcomes over the 15-month field trial, shared six pedagogical features—autonomous learning, motor skills, task structure, engagement, language demand and personalisation. Results demonstrate that this combination of features is effective at supporting learning of foundational skills and has a positive impact on educational outcomes. To maximise learning potential in environments with both limited resources and deployment opportunities, developers should focus attention on this combination of features, especially for out-of-school children in low- and middle-income countries.
Practitioner notes
What is already known about this topic- Interactive apps are becoming common to support foundational learning for children both in and out of school settings.
- The Global Learning XPRIZE competition demonstrates that learning apps can facilitate learning improvements in out-of-school children living in sub-Saharan Africa.
- To understand which app features are most important in supporting learning in these contexts, we need to establish which pedagogical features were shared by the winning apps.
- Effective learning of foundational skills can be achieved with a range of pedagogical features.
- To maximise learning, apps should focus on combining elements of autonomous learning, motor skills, task structure, engagement, language demand and personalisation.
- Free Play is not a key pedagogical feature to facilitate learning within this context.
- When developing learning apps with primary-aged, out-of-school children in low-income contexts, app developers should try to incorporate the six key features associated with improving learning outcomes.
- Governments, school leaders and parents should use these findings to inform their decisions when choosing an appropriate learning app for children.
19.
Mohammed Saqr 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1077-1094
Learning analytics is a fast-growing discipline. Institutions and countries alike are racing to harness the power of using data to support students, teachers and stakeholders. Research in the field has proven that predicting and supporting underachieving students is worthwhile. Nonetheless, challenges remain unresolved, for example, lack of generalizability, portability and failure to advance our understanding of students' behaviour. Recently, interest has grown in modelling individual or within-person behaviour, that is, understanding the person-specific changes. This study applies a novel method that combines within-person with between-person variance to better understand how changes unfolding at the individual level can explain students' final grades. By modelling the within-person variance, we directly model where the process takes place, that is the student. Our study finds that combining within- and between-person variance offers a better explanatory power and a better guidance of the variables that could be targeted for intervention at the personal and group levels. Furthermore, using within-person variance opens the door for person-specific idiographic models that work on individual student data and offer students support based on their own insights.
Practitioner notes
What is already known about this topic- Predicting students' performance has commonly been implemented using cross-sectional data at the group level.
- Predictive models help predict and explain student performance in individual courses but are hard to generalize.
- Heterogeneity has been a major factor in hindering cross-course or context generalization.
- Intra-individual (within-person) variations can be modelled using repeated measures data.
- Hybrid between–within-person models offer more explanatory and predictive power of students' performance.
- Intra-individual variations do not mirror interindividual variations, and thus, generalization is not warranted.
- Regularity is a robust predictor of student performance at both the individual and the group levels.
- The study offers a method for teachers to better understand and predict students' performance.
- The study offers a method of identifying what works on a group or personal level.
- Intervention at the personal level can be more effective when using within-person predictors and at the group level when using between-person predictors.
20.
Chimezie O. Amaefule Jasmin Breitwieser Daniel Biedermann Lea Nobbe Hendrik Drachsler Garvin Brod 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1351-1372
Educational applications (apps) offer opportunities for designing learning activities children enjoy and benefit from. We redesigned a typical mobile learning activity to make it more enjoyable and useful for children. Relying on the technology acceptance model, we investigated whether and how implementing this activity in an app can increase children's intention to use. During the 27-day study, children (N = 103, 9–14 years) used the app to memorize one-sentence learning plans each day. Children used three different app-based learning activities throughout the study. In two standard activities, children reread or reassembled the words of the plan. In the redesigned activity, children represented the meaning of the plan with emojis. Children repeatedly reported on their attitude towards each activity. Subsequently, children reported perceived enjoyment and intention to use the app. Results showed children found the emoji activity most enjoyable, and enjoyment of the emoji activity contributed uniquely towards intention to use. Additionally, children's enjoyment of the app mediated their intention to use the app in the future. Overall, the study suggests that children's enjoyment of an app is crucial in predicting their subsequent intention to use, and it provides a concrete example of how emojis can be used to boost enjoyment.
Practitioner notes
What is already known about this topic- Educational applications provide children with unrestricted access to mobile learning resources.
- Positive attitudes towards educational applications predict behavioural intention to use these applications, at least in young adults.
- There is a need for more research examining the relevance of enjoyable learning activities in fostering children's sustained usage of an educational application.
- Positive attitude towards the use of emojis during learning activities uniquely contributed to children's behavioural intention to use the application.
- Perceived enjoyment predicted behavioural intention to use the application.
- Perceived enjoyment mediated the effect of attitude towards using learning activities on the behavioural intention to use the mobile educational application.
- These findings highlight the importance of enjoyment for children's' acceptance of educational applications.
- Enjoyable learning activities are necessary to ensure sustained usage of educational applications.
- The paper provides a concrete example of how emojis can be used to boost enjoyment of a typical mobile learning activity.