Artificial intelligence (AI) is transforming education, with the potential to revolutionize teaching, learning, and school administration. However, the successful implementation of AI-driven initiatives involves more than simply adopting new technology but requires a thorough understanding of how school leaders perceive AI and their preparedness to integrate it into educational planning and practices. Principals and school leaders play a critical role in supporting effective instruction and student achievement across different content areas, but their needs and challenges, particularly regarding AI integration, are often overlooked. Understanding school leaders' perceptions of AI is vital for successfully implementing AI in schools. This exploratory study employed a qualitative approach to investigate school leaders' perspectives on AI integration in K-12 school settings. Two 90-minute semi-structured focus groups were conducted. Inductive analysis was performed to interpret the qualitative data. The findings highlighted school leaders' perceptions of best practices of AI integration as well as their concerns about AI and the associated ethical dilemma. This collective knowledge of potential solutions for AI integration can help establish a systematic, top-down approach in K-12 school settings.

This research sought to answer the question about what students learnt from a self-regulated learning (SRL) video annotation tool in a hybrid secondary acting classroom. SRL is an important skill for students to self-direct their own learning processes. For the intervention, students engaged in a series of SRL activities through the video annotation tool, VideoAnt, for six weeks during the distance learning period. A convergent parallel mixed methods design was conducted. Quantitative data was collected using the International Thespian Society – Acting Rubric to assess students’ acting skills. Qualitative data was collected via semi-structured individual interviews. Quantitative findings demonstrated students gained proficiency in acting skills after using the video annotation tool in the acting classroom. Interview data about participants’ perceptions of the intervention were analyzed to triangulate quantitative findings. Implications of these findings for theatre/art educators and academics investing in SRL are discussed.

With continued growth in online learning, motivation remains a key factor in persistence and achievement. Online mathematics students struggle with self-regulation and self-efficacy. As reported by Ryan and Deci (Self-determination theory: basic psychological needs in motivation, development, and wellness, Guilford Press, 2017, https://doi.org/10.3233/EFI-2004-22201 ), in their well-established self-determination theory, contended that satisfying the psychological needs of autonomy (involving self-regulation), competence (involving self-efficacy), and relatedness (involving a sense of belonging) creates a suitable environment for integrated extrinsic and intrinsic motivation to thrive. The purpose of this action research was to implement a self-determination theory-based online unit for mathematics students to improve their motivation levels. A convergent mixed methods action research design was employed to identify changes in the levels of autonomy, competence, and relatedness of the participants in an Algebra 2 course (n = 50) at a fully online school in the northeastern United States. Results from the motivation questionnaire and student interviews indicated a significant increase in competence and relatedness after completing the intervention. While no significant increase in autonomy was evident in the quantitative results, the qualitative findings showed some support for improved autonomy. Recommendations for online mathematics course design to support increased motivation are provided.

Blended learning has been widely integrated in college-level computer science education. Despite evidence about benefits of blended learning, students' in-class activities remain underexplored. To afford effective blended learning experience, supporting students in both modalities is essential. This study thus took an initial step to fill the gap by investigating college students' in-class activities in a blended course from the perspective of attention. Using non-intrusive electroencephalography (EEG) instruments to collect attentional data, this study found students' attention in in-class activities positively correlated with their learning gains. Students' attention also varied across in-class activities, reaching a higher level in group discussions than in pre-tests and lectures. Linear regression analysis indicated students' length of time spent viewing online resources and their pre-test scores significantly predicted their in-class attention. The findings of the study provide insight into course design and facilitation for effective blended computer science courses.

Self-regulated learning is a crucial skill that may enable massive numbers of learners to thrive in MOOCs, but MOOC learners differ in their self-regulated learning skills, as low self-regulated learners need support to regulate their learning process in MOOCs. Designing self-regulated learning scaffoldings builds upon an accurate appraisal of learners’ self-regulated learning and also a person-centered understanding of self-regulated learner profiles in MOOCs. Therefore, this study applied a two-parameter item response theory model to accurately evaluate online learners’ self-regulated learning in MOOCs and then performed a cluster analysis to identify learner profiles in terms of their traits in each phase of self-regulated learning. The findings of this study identified five clusters of self-regulated learner profiles, upon which a further statistical analysis result indicated that the learners with lower forethought skills might be less committed to completing course assignments. Implications for self-regulated learning in MOOCs are discussed at the end.

Artificial intelligence (AI) has the potential to transform education with its power of uncovering insights from massive data about student learning patterns. However, ethical and trustworthy concerns of AI have been raised but are unsolved. Prominent ethical issues in high school AI education include data privacy, information leakage, abusive language, and fairness. This paper describes technological components that were built to address ethical and trustworthy concerns in a multi-modal collaborative platform (called ALLURE chatbot) for high school students to collaborate with AI to solve the Rubik's cube. In data privacy, we want to ensure that the informed consent of children, parents, and teachers, is at the center of any data that is managed. Since children are involved, language, whether textual, audio, or visual, is acceptable both from users and AI and the system can steer interaction away from dangerous situations. In information management, we also want to ensure that the system, while learning to improve over time, does not leak information about users from one group to another.

Blended learning, integrating online and in-person components, has been increasingly adopted in higher education to enhance students’ learning experience and outcomes. While the advantages of blended learning are well-evidenced, research has primarily focused on the online pre-learning component, neglecting the significance of in-class activities. In-class activities play a crucial role in affording active learning opportunities (e.g., discussion, elaboration), necessitating a systemic understanding of their dynamics. The purpose of this study was thus to systemically investigate college students’ learning behaviors during in-class activities in a blended course. In-class activities were video-recorded and labelled manually following a coding scheme. By establishing a linear regression model, the study identified listening to the instructor’s lecture and taking notes as two predictors of students’ learning gains. Additionally, sequential patterns of learning behaviors during in-class activities were examined. The reciprocal interactions between students’ behavior of listening to the lecture and their note-taking actions were noted. The findings of this study contributed to a systemic view of blended learning by shedding light on students’ learning behaviors and their implications for instructional practice.

Collaborative problem-solving (CPS) involves the interaction and interdependence of students’ social and cognitive skills, making it a complex learning process. To delve into the complex dynamics of CPS, previous research has categorized socio-cognitive roles, providing insights into social-cognitive frameworks. However, despite the specific cognitive and social interaction structures employed by roles to engage in CPS interactions, most existing research primarily focuses on individual roles, neglecting inter-role interactions. To fill this gap, twelve triad groups were formed by engaging 36 undergraduate students in online CPS activities to examine differences in social and cognitive interaction structures across different roles and group compositions. Additionally, analyze the differences in CPS processes among various group compositions. The analyses identified five roles ( Lurkers, Followers, Drivers, Influential Actors , and Innovators ) and three group compositions ( Balanced groups, Decentralized groups , and Power Struggle groups ). The socio-cognitive structure of Balanced groups , along with other evidence, indicates effective information sharing and negotiation interactions. In contrast, Decentralized and Power Struggle groups exhibited various deficiencies in their socio-cognitive structures, negatively impacting group collaboration processes. These insights provide educators with a comprehensive guide to fostering effective group compositions and role dynamics in online CPS settings, thereby enhancing the overall success of CPS. Additionally, possible activity design considerations and scaffolding strategies are also discussed.

Self-regulated learning (SRL) has been viewed as a critical skill for learner success in MOOCs, but due to methodological limitations of self-reported measures, the relationship between SRL and learner performance remains unquantified. Therefore, this study sought to investigate how SRL impacted learner performance in MOOCs by linking clickstream data about SRL to learner performance variables. Following a social cognitive perspective of SRL, learner traces were categorized into three phases including forethought, performance/volition control, and self-reflection phase. Then separate multiple linear regression analyses were performed to examine the relationship between SRL traces in each phase and learner performance variables (e.g., quizzes scores and forum participation record). The findings of this study indicated that SRL traces in performance/volition control and self-reflection phases positively predict learners' quizzes scores. In addition, SRL traces in each of the three phases were significantly correlated to learners' forum participation, despite a small effect of the model. Particularly, self-reflective traces negatively predicted learners' forum participation but SRL traces in the other two phases had a positive relationship with the outcome variable. Practical implications about supporting learners in MOOCs are discussed in the end.

Integrating computer science (CS) content into existing STEM curricula emerges as a viable solution to broadening rural students’ participation in CS, but rural students have a significant proficiency gap in science and mathematics. By focusing on an English language arts course as the context for CS integration, this exploratory study examined the impact of a robotics-based intervention integrated into a high school language arts class on rural students’ CS self-efficacy. A convergent mixed methods design was applied to collect and analyze qualitative and qualitative data separately. Quantitative results confirmed that the integrated robotics-based intervention significantly improved rural students’ CS self-efficacy. Qualitative findings provided insights on how embodied learning and mastery experience facilitated by the robotics-based intervention fostered rural students’ CS self-efficacy. The findings from the two sources of data were integrated, shedding light on the potential of curricular integration in language arts classes for broadening rural student participation in CS education.

Practitioner-focused educational doctoral programs have grown substantially in recent years. Dissertations in Practice (DiPs), which are the culminating research report and evaluation method in these programs, differ from traditional PhD dissertations in their focus on addressing a problem of practice and on connecting theories with practice. As part of our ongoing program evaluation, we reviewed DiPs from doctoral students who graduated from an online asynchronous Educational Doctoral program in Learning Design and Technologies at the University of South Carolina. Findings revealed that most students chose a pragmatic philosophical paradigm, adopted a mixed methods research design, reported an action research intervention implemented with populations in K-12 schools, used surveys and interviews as data sources, and analyzed data with descriptive/inferential statistics and thematic analysis. Implications for the program curriculum are discussed.

The current study examined the influences of self-regulated learning prompts provided during three different self-regulated learning phases in video-based learning. A total of 58 college students from a midwestern university were randomly assigned into one of the four conditions: (1) self-regulated learning prompts at the forethought phase, (2) self-regulated learning prompts at the performance phase, (3) self-regulated learning prompts at the self-reflection phase, and (4) no self-regulated learning prompts in any phase. Participants watched a video on the human respiratory system and responded to the self-regulated learning prompts in one of the three phases or no prompts. Upon completing the video, participants responded to a questionnaire assessing their self-regulated learning levels and learning outcome. Self-regulated learning levels were also inferred by learners' behavior of pausing and rewinding during video-watching. Results indicated that participants who received self-regulated learning prompts at the performance phase achieved better learning outcome compared to those in the no prompt condition.

It's critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students' data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students' processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies.

Socially shared metacognition is important for effective collaborative problem solving in virtual laboratory settings, A holistic account of socially shared metacognition in virtual laboratory settings is needed to advance our understanding, but previous studies have only focused on the isolated effect of each dimension on problem solving. This study thus applied learning analytics techniques to develop a comprehensive understanding of socially shared metacognition during collaborative problem solving in virtual laboratories. We manually coded 126 collaborative problem-solving scenarios in a virtual physics laboratory and then employed K-Means clustering analysis to identify patterns of socially shared metacognition. Four clusters were discovered. Statistical analysis was performed to investigate how the clusters were associated with the outcome of collaborative problem solving and also how they related to the difficulty level of problems. The findings of this study provided theoretical implications to advance the understanding of socially shared metacognition in virtual laboratory settings and also practical implications to foster effective collaborative problem solving in those settings.

Laboratory experience is critical to foster college students' collaborative problem-solving (CPS) abilities, but whether students stay cognitively engaged in CPS tasks during online laboratory sessions remains unknown. This study applied multimodal data analysis to examine college students' (N = 36) cognitive engagement in CPS during their online experimentation experience. Groups of three collaborated on CPS tasks via shared worksheets and computer-based simulations on videoconferences. Portable electroencephalogram instruments were used to determine students' levels of cognitive engagement in CPS activities. The multimodal data analysis (e.g., electroencephalogram, surveys, and artifacts) results showed a significant difference in students' cognitive engagement between different phases of CPS. The students' cognitive engagement significantly differed between groups who did and did not complete the task. Additionally, intrinsic motivation predicted students' cognitive engagement in the completion groups while self-efficacy was the primary predictor of cognitive engagement for the groups who did not complete the task.

The low retention rate becomes a scale-efficiency tradeoff for Massive Open Online Courses (MOOCs). To resolve this tradeoff, understanding learner experience of successfully completing MOOCs is necessary. Keen completers , who complete a MOOC and meet the requirement of passing the course, tend to actively participate in course interactions; however, their voices about how interactions contribute to course completion are unheard. Therefore, this study applied a qualitative methodology to explore keen completers' interaction experience and their perceptions of various types of interaction (e.g. learner-content, learner-learner, learner-instructor, learner-interface, learner-self, and learner-exterior interaction) in a MOOC. The findings of this study have added keen completers' voices to existing evidence about interactions with a focus on how each type of interaction aided in the completion of a MOOC. Practical implications about maintaining learners' effective interaction experience in MOOCs are discussed.

Collaborative Problem Solving (CPS) has received increasing attention for its role in promoting learners' cognitive and social development in STEM education. However, little is known about how learners interact dynamically within a group at different time granularities. This gap mainly resulted from overlooking the time dimension of interactions, leading to a lack of nuanced understanding of moment-to-moment interaction in CPS. In this study, we demonstrated the potential of temporal group interaction density in modeling online CPS interactions and investigated the impact of temporal interaction density on CPS processes and outcomes. Specifically, we proposed using cumulative weighted density to measure the holistic state of group interactions and explained the differences in group interactions with different collaborative performance and interaction densities by modeling the transition and evolution of interaction sequences through Apriori and cumulative relative centrality. Results indicated that group interaction density cannot directly predict their collaborative performance, but notable differences in interaction patterns existed in the high-performance groups with different interaction densities, while low-performance groups showed interactive commonalities towards the completion of CPS. The findings of this study guided the design of CPS interventions and supported the process mining of CPS interactions, with vital practical implications for CPS assessment and skills development.

The purpose of this qualitative case study was to develop an account of teachers’ perception of barriers to technology integration throughout distance learning. COVID-19 pandemic forced schools to adopt distance learning to cope with the crisis, but whether teachers are prepared for this change is unknown. Therefore, this study described teachers’ experience of technology integration over the course of distance learning and identified the barriers they faced at a small, private school for students with dyslexia. The findings found distance learning influenced teachers’ technological knowledge, attitudes, and beliefs about technology integration. Barriers to technology integration were identified by all participants, but only novice integrators and one intermediate integrator experienced second-order barriers. Implications are provided on supporting teachers to overcome barriers towards technology integration.

Evidence that open educational resources (OER) can decrease college students' educational cost without harm to their course performance on different subjects has been well documented, but student motivation to use OER for learning is underexplored. This study investigated college students' achievement goals of using OER from the perspective of expectancy-value theory. We recorded the survey responses of 246 college students in an education course at a public university in the southeast of the United States of America for analysis. We established a structural equation model to investigate the relationship between their task value beliefs in using OER and their course achievement goals. The findings show that the students' perceived usefulness of OER positively predicted their mastery-approach goals but negatively predicted their performance-approach goals. In addition, their self-estimated cost of learning with OER predicted their mastery-avoidance goals. We discuss practical implications for facilitating college students' motivation toward using OER.