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.

     

Keep DRAGging ON: Is solving more problems in DragonBox 12+ associated with higher mathematical performance during the COVID-19 pandemic?

Prior research has shown that game-based learning tools, such as DragonBox 12+, support algebraic understanding and that students' in-game progress positively predicts their later performance. Using data from 253 seventh-graders (12–13 years old) who played DragonBox as a part of technology intervention, we examined (a) the relations between students' progress within DragonBox and their algebraic knowledge and general mathematics achievement, (b) the moderating effects of students' prior performance on these relations and (c) the potential factors associated with students' in-game progress. Among students with higher prior algebraic knowledge, higher in-game progress was related to higher algebraic knowledge after the intervention. Higher in-game progress was also associated with higher end-of-year mathematics achievement, and this association was stronger among students with lower prior mathematics achievement. Students' demographic characteristics, prior knowledge and prior achievement did not significantly predict in-game progress beyond the number of intervention sessions students completed. These findings advance research on how, for whom and in what contexts game-based interventions, such as DragonBox, support mathematical learning and have implications for practice using game-based technologies to supplement instruction.

Practitioner notes

What is already known about this topic

• DragonBox 12+ may support students' understanding of algebra but the findings are mixed.
• Students who solve more problems within math games tend to show higher performance after gameplay.
• Students' engagement with mathematics is often related to their prior math performance.

What this paper adds

• For students with higher prior algebraic knowledge, solving more problems in DragonBox 12+ is related to higher algebraic performance after gameplay.
• Students who make more in-game progress also have higher mathematics achievement, especially for students with lower prior achievement.
• Students who spend more time playing DragonBox 12+ make more in-game progress; their demographic, prior knowledge and prior achievement are not related to in-game progress.

Implications for practice and/or policy

• DragonBox 12+ can be beneficial as a supplement to algebra instruction for students with some understanding of algebra.
• DragonBox 12+ can engage students with mathematics across achievement levels.
• Dedicating time and encouraging students to play DragonBox 12+ may help them make more in-game progress, and in turn, support math learning.

     

Assessing behavioral data science privacy issues in government artificial intelligence deployment

In today's global culture where the Internet has established itself as the main tool for communication and commerce, the capability to massively analyze and predict citizens' behavior has become a priority for governments in terms of collective intelligence and security. At the same time, in the context of novel possibilities that artificial intelligence (AI) brings to governments in terms of understanding and developing collective behavior analysis, important concerns related to citizens' privacy have emerged. In order to identify the main uses that governments make of AI and to define citizens' concerns about their privacy, in the present study, we undertook a systematic review of the literature, conducted in-depth interviews, and applied data-mining techniques. Based on our results, we classified and discussed the risks to citizens' privacy according to the types of AI strategies used by governments that may affect collective behavior and cause massive behavior modification. Our results revealed 11 uses of AI strategies used by the government to improve their interaction with citizens, organizations in cities, services provided by public institutions or the economy, among other areas. In relation to citizens' privacy when AI is used by governments, we identified 8 topics related to human behavior predictions, intelligence decision making, decision automation, digital surveillance, data privacy law and regulation, and the risk of behavior modification. The paper concludes with a discussion of the development of regulations focused on the ethical design of citizen data collection, where implications for governments are presented aimed at regulating security, ethics, and data privacy. Additionally, we propose a research agenda composed by 16 research questions to be investigated in further research.     

The role of linguistic features in science and math comprehension and performance: A systematic review and desiderata for future research

There is a growing body of research on the role of linguistic features (LF) in comprehension and performance in science and math. Of particular interest is which LF facilitate or hinder comprehension. In this systematic review, we provide an overview of findings on LF at the word, sentence and text levels, on comprehension and performance in science and math. Our literature search revealed n = 40 articles included in this review. Overall, the role of LF in comprehension and performance in science and math is complex, with findings varying across the different LF. For each LF, we discuss the findings and uncover remaining questions. In the general discussion, we uncover strengths and weaknesses of previous research and discuss open questions before making eight recommendations and identifying tasks for future research aiming to understand the complex nature of LF.     

What makes or breaks competitive research proposals? A mixed-methods analysis of research grant evaluation reports

The evaluation of grant proposals is an essential aspect of competitive research funding. Funding bodies and agencies rely in many instances on external peer reviewers for grant assessment. Most of the research available is about quantitative aspects of this assessment, and there is little evidence from qualitative studies. We used a combination of machine learning and qualitative analysis methods to analyse the reviewers' comments in evaluation reports from 3667 grant applications to the Initial Training Networks (ITN) of the Marie Curie Actions under the Seventh Framework Programme (FP7). Our results show that the reviewers' comments for each evaluation criterion were aligned with the Action's prespecified criteria and that the evaluation outcome was more influenced by the proposals’ weaknesses than by their strengths.     

Taking the maker movement to school: A systematic review of preK-12 school-based makerspace research

In this systematic review, we examined research on school-based makerspaces, emergent but increasingly popular sites for instruction and learning in preK through 12 settings. Through electronic database, hand, and ancestral searches, we identified 22 empirical studies published in peer-reviewed journals and dissertations that reported preK-12 students’ learning outcomes after participating in school-based makerspace interventions. We found that school-based makerspace research is increasing and published internationally, with a majority of studies (n = 13) conducted with middle and high school participants. Outcomes and interventions varied considerably across studies, demonstrating the disparate nature of school-based makerspace research. In the studies we reviewed, the goals, objectives, and scope of makerspace interventions did not conflict with those of schools, but best practices for makerspace teachers were lacking and equity-oriented approaches to designing makerspace activities and materials were still emerging. Implications of our findings for planning makerspace instruction and future research on makerspace interventions are discussed.     

A systematic literature review of K-12 environmental Citizen Science (CS) initiatives: Unveiling the CS pedagogical and participatory aspects contributing to students’ environmental citizenship

Environmental Citizen Science (CS) initiatives have been largely embraced in K-12 education, as they are often hypothesized to improve students' knowledge, skills, attitudes, and behaviours to act as “environmental citizens” according to the notion of Environmental Citizenship (EC). However, the potential of environmental CS initiatives to promote Education for Environmental Citizenship (EEC) has not been systematically explored. At the same time, environmental CS initiatives for educational purposes are highly heterogenous and learning is enacted in diverse ways, according to the participatory and the pedagogical components underpinning each initiative. To address the complexity of the field, this review study adopts the PRISMA methodology to synthesize thirty-four empirical studies (n = 34) retrieved from a systematic review of the literature covering the last two decades (2000–2020). The reviewed environmental CS initiatives were subjected to a content analysis to identify their impact on students' EC (e.g., EC competences, actions, outcomes), as well as to unveil the CS initiatives' constitutional components in terms of (a) Participation (e.g., types of students' contributions, level of data collection, frequency of students' participation, modes of student engagement, forms of students’ involvement), and (b) Pedagogy (e.g., learning goals, educational contexts, learning mechanisms, EEC pedagogy). Our analysis shed light to the three territories (Participation, Pedagogy, Environmental Citizenship) underpinning the reviewed CS initiatives as well as to their interrelations. We reflect on these findings, and we provide directions for future research to guide the development of more successful environmental CS initiatives in K-12 education, serving as a vehicle for EC.     

Fair and Explainable Depression Detection in Social Media

Detection at an early stage is vital for the diagnosis of the majority of critical illnesses and is the same for identifying people suffering from depression. Nowadays, a number of researches have been done successfully to identify depressed persons based on their social media postings. However, an unexpected bias has been observed in these studies, which can be due to various factors like unequal data distribution. In this paper, the imbalance found in terms of participation in the various age groups and demographics is normalized using the one-shot decision approach. Further, we present an ensemble model combining SVM and KNN with the intrinsic explainability in conjunction with noisy label correction approaches, offering an innovative solution to the problem of distinguishing between depression symptoms and suicidal ideas. We achieved a final classification accuracy of 98.05%, with the proposed ensemble model ensuring that the data classification is not biased in any manner.