Generation Alpha students will enter higher education with learning expectations shaped by digital-first, multimodal, and artificial intelligence-inflected environments. This article contends that higher education cannot teach these students effectively by simply transferring legacy lecture-based practices to digital platforms.

Smart learning environments provide students with opportunities to engage in self-regulated learning (SRL). However, little research has examined how teachers leverage these opportunities. We employed a multiple-case study methodology to examine the SRL supporting instructional practices of five third-grade teachers as they implemented a science unit embedded within a smart learning environment which enabled student engagement in SRL processes. The identified set of instructional practices was used to formulate the teachers’ SRL support profiles, which indicates the extent to which teachers provided students with opportunities to regulate the cognitive aspect of SRL. Our findings indicated that teachers’ interactions with the smart learning environment varied from teacher-focused use, restricting opportunities for SRL engagement, to student-focused use, leveraging opportunities for student engagement in SRL. These results demonstrate that, while smart learning environments potentially provide rich contexts for students’ engagement in SRL, how teachers use this environment through their SRL-supporting instructional practices determines students’ actual engagement in SRL.

The study investigates the impact of Roadmap-formatted curriculum on student reading growth in grades 3-5 in the Ypsilanti Community Schools (Ypsilanti, MI). Classrooms in grades 3-5 were divided into two groups: those using the Roadmap-formatted, commercially-provided curriculum and those using the same commercially-provided curriculum in traditional format (e.g., primarily paper-and-pencil format.) Results, measured via NWEA Reading Conditional Growth Percentiles (CGP), revealed a 9-percentage-point improvement in reading growth among students whose teachers who used Roadmaps-formatted curriculum with their students. These findings suggest that deeply-digital, interactive curriculum significantly enhance reading achievement, aligning with contemporary digital learning theories and addressing the needs of Generation Alpha learners.

Current curricula and pedagogy need to change to effectively support the learning needs of the Alphas and iGens. The Alpha Generation, children born after 2012, known as the “digital-first” generation, have grown up on hand-held, digital screens not watching television or reading paper-based books. Similarly, the iGens, also known as Gen Zs, university students born between 1995 and 2012, are the first generation to spend their entire adolescence in the age of the smartphone. "With social media and texting replacing other activities, iGen spends less time with their friends in person” (Twenge, 2017). The COVID disruption further pushed the Alphas and iGens onto screens for social interaction and learning. Returning to paper-and-pencil curricula and pedagogy does not serve the needs of these generations. The President’s Council of Advisors on Science and Technology (PCAST) (2010) issued a prescient report describing the potential for deeply-digital curricula to “provide a richer and more engaging experience through interactive components, videos, simulations, hyperlinks …” (p. 77). Alphas' and iGens' deeply-digital experiences outside of school have them expecting deeply-digital learning experiences inside of school. Toward addressing the learning needs of Alphas in Kindergarten through sixth grade and iGen university students, we have been studying how deeply-digital, highly-interactive curricula plus digitally-motivated, pedagogical practices can increase their engagement and achievement in both in-class and remote learning environments.

1 Saginaw Valley State University (UNITED STATES)2 University of Michigan (UNITED STATES)3 University of North Texas (UNITED STATES)

Purpose: Early diagnosis and treatment of retinoblastoma are of paramount importance for a positive clinical outcome. The most common sign of retinoblastoma is leukocoria, or white pupil. Effective, easy-to-perform, community-based screening is needed to improve outcomes in lower-income regions. The EyeScreen (developed by Joshua Meyer from the University of Michigan) Android (Google LLC) smartphone application is an important step toward addressing this need. The purpose of this study was to examine the potential of the novel use of low-cost technologies—a cell phone application and machine learning—to identify leukocoria. Design: A cell phone application was developed and refined with the feedback from on-site, single-population use in Ethiopia. Application performance was evaluated in this technology validation study. Participants: One thousand four hundred fifty-seven participants were recruited from ophthalmology and pediatric clinics in Addis Ababa, Ethiopia. Methods: Photographs obtained with inexpensive Android smartphones running the EyeScreen Application were used to train an ImageNet (ResNet) machine learning model and to measure the performance of the app. Eighty percent of the images were used in training the model, and 20% were reserved for testing. Main Outcome Measures: Performance of the model was measured in terms of sensitivity, specificity, receiver operating characteristic (ROC) curve, and precision-recall curve. Results: Analyses of the participant images resulted in the following at the participant level: sensitivity, 87%; specificity, 73%; area under the ROC curve, 0.93; and area under the precision-recall curve, 0.77. Conclusions: EyeScreen has the potential to serve as an effective screening tool in the areas of the world most affected by delayed retinoblastoma diagnosis. The relatively high initial performance of the machine learning model with small training datasets in this early-phase study can serve as a proof of concept for future use of machine learning and artificial intelligence in ophthalmic applications.

C. Norris1, E. Soloway2 1University of North Texas (UNITED STATES) 2University of Michigan (UNITED STATES)

In line with the substantial interest in STEM (science, technology, engineering, and mathematics) education and the major projects in STEM curriculum development around the world, efforts should be particularly made to increase the supply of STEM teachers through proper and effective teacher professional development. Although there have been a number of studies related to teacher professional development for individual subject training in science, technology, engineering and mathematics, quality research on professional development for teachers to develop their capacity for adopting the integrative and cross-disciplinary approaches advocated in STEM education remains in its infancy. The theme of this special issue is two-fold: (a) to provide researchers and practitioners in STEM education with a scholarly platform for reflecting on what challenges and impediments STEM teachers have encountered, and (b) to exchange new theoretical and practical insights gained from empirical research on designing, enacting and evaluating professional development programmes for building teachers' capacity in STEM education.

For the past few years our group has worked on systems for automatic diagnosis of non-syntactic errors in novices' computer programs (Soloway, et al., 1983; Johnson, 1986; Sack, in press). Such errors (or "bugs") go beyond simple mistakes in the language syntax; they reflect deeper misunderstandings of how an algorithm works and how it should be implemented in that language. Our automatic debuggers are on-line help facilities designed to find such errors in students' programs. To be effective, an automatic program debugger must have methods allowing it to both: Identify errors in computer programs, and Explain to the student the errors it has found.

Appears in: EDULEARN21 Proceedings Publication year: 2021Page: 3013 (abstract only)ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.0640Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference

Seeking to change computing teaching to improve computer science.

The notion of scaffolding learners to help them succeed in solving problems otherwise too difficult for them is an important idea that has extended into the design of scaffolded software tools for learners. However, although there is a growing body of work on scaffolded tools, scaffold design, and the impact of scaffolding, the field has not yet converged on a common theoretical framework that defines rationales and approaches to guide the design of scaffolded tools. In this article, we present a scaffolding design framework addressing scaffolded software tools for science inquiry. Developed through iterative cycles of inductive and theory-based analysis, the framework synthesizes the work of prior design efforts, theoretical arguments, and empirical work in a set of guidelines that are organized around science inquiry practices and the challenges learners face in those practices. The framework can provide a basis for developing a theory of pedagogical support and a mechanism to describe successful scaffolding approaches. It can also guide design, not in a prescriptive manner but by providing designers with heuristics and examples of possible ways to address the challenges learners face.

Mobile learning has continued to evolve since our state-of-the-art assessment in the first edition of this Handbook. As documented in the "mobile learning" section of this 2nd edition of the Handbook, (1) mobile learning per se is seeing limited adoption in classrooms in the USA, the UK, and in 17 countries in Asia, (2) while the young learners themselves are adopting mobile learning in a significant way outside the classroom. While the developing nations struggle with issues of device access, the developed nations struggle with seeing the value of mobile learning with respect to increased student achievement. Thus, while other educational technology trends (e.g., personalized learning, flipped learning, online learning) are attracting attention, mobile learning continues to evolve below the "radar" - with a comeback into the classroom occurring when educators better understand the significant impact mobile learning is having on this generation of young mobile learners.

Mobile learning and mobile technologies have garnered a significant amount of attention in K-12. As we define it, mobile learning is about 24/7, all-the-time, everywhere learning that is supported by mobile technologies. And, as we argue that mobile learning is increasingly a dominant form of learning, we look to better understand the roadblocks that are holding back its adoption in K-12. To that end, then, we look back in time and we look forward in time to identify the issues involved in mobile learning adoption in K-12. Rooted then in that analysis, we are optimistic thatmobile learning can and will be adopted inK-12 over the next 5 years.

Mobile learning has continued to evolve since our state-of-the-art assessment in the first edition of this Handbook. As documented in the “mobile learning” section of this 2nd edition of the Handbook, (1) mobile learning per se is seeing limited adoption in classrooms in the USA, the UK, and in 17 countries in Asia, (2) while the young learners themselves are adopting mobile learning in a significant way outside the classroom. While the developing nations struggle with issues of device access, the developed nations struggle with seeing the value of mobile learning with respect to increased student achievement. Thus, while other educational technology trends (e.g., personalized learning, flipped learning, online learning) are attracting attention, mobile learning continues to evolve below the “radar” – with a comeback into the classroom occurring when educators better understand the significant impact mobile learning is having on this generation of young mobile learners.