Learning Sciences Research

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https://hdl.handle.net/20.500.12265/2

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Cyberlearning Community Report: The State of Cyberlearning and the Future of Learning with Technology
(SRI International, 2017) Roschelle, Jeremy; Martin, Wendy; Ahn, June; Schank, Patricia
Cyberlearning researchers envision and investigate the future of learning with technology. In an earlier generation of research, the theoretical focus was on students’ reasoning, the standard technology was a laptop or desktop computer, and the typical setting was a conventional classroom. Such research remains tremendously important. However, emerging frontiers in the learning sciences now call on cyberlearning research to develop new theories, investigate developing technological capabilities, and consider diverse education settings. This report, organized by CIRCL and co-authored by 22 members of the U.S. cyberlearning community, describes six design themes emerging across multiple NSF-funded cyberlearning projects.
Meeting Learners Where They Are: Using Microsoft Forms to Drive Improvement in Learning Outcomes
(Digital Promise, 2018) Peters, Vanessa
Enabling Analytics for Improvement: Lessons from Year 2 of Fresno’s Personalized Learning Initiative
(Digital Promise, 2018) Peters, Vanessa; Means, Barbara; Langworthy, Maria; Neufeld, Phil; Coe, Ryan; Meehan, Kenneth; Smith, Stevin
Overview of Data Mining’s Potential Benefits and Limitations in Education Research
(Practical Assessment, Research & Evaluation, 2018-10) Iwatani, Emi
Education researchers are increasingly interested in applying data mining approaches, but to date, there has been no overarching exposition of their methodological advantages and disadvantages to the field. This is partly because the use of data mining in education research is relatively new, so its value and consequences are not yet well understood. Yet statisticians, sociologists and those who study computer-based education have discussed the methodological merits of data mining in education research. This article brings together their perspectives, providing an interdisciplinary overview of potential benefits and drawbacks. The benefits, regardless of scholar background, largely emphasize the speed and ease with which data mining approaches can help explore very large datasets. Perceived drawbacks, however, differ based on disciplinary expertise. For example, statisticians question data mining’s exploratory nature and non-reliance on sampling theory, while sociologists raise concerns about an excessive reliance on data in research designs and in understandings of education.
Broadening Participation in STEM College Majors: Effects of Attending a STEM-Focused High School
(AERA Open, 2018-11) Means, Barbara; Wang, Haiwen; Wei, Xin; Iwatani, Emi; Peters, Vanessa
To increase participation in science, technology, engineering, and mathematics (STEM) studies and careers, some states have promoted inclusive STEM high schools. This study addressed the question of whether these high schools improve the odds that their graduates will pursue a STEM major in college. State higher education records were obtained for students surveyed as seniors in 23 inclusive STEM high schools and 19 comparison schools without a STEM focus. Propensity score weighting was used to ensure that students in the comparison school sample were very similar to those in the inclusive STEM school sample in terms of demographic characteristics and Grade 8 achievement. Students overall and from under-represented groups who had attended inclusive STEM high schools were significantly more likely to be in a STEM bachelor’s degree program two years after high school graduation. For students who entered two-year colleges, on the other hand, attending an inclusive STEM high school was not associated with entry into STEM majors.
Transforming Teachers’ Knowledge for Teaching Mathematics with Technologies through Online Knowledge-Building Communities
(University of South Carolina & Clemson University, 2018-11) Niess, Margaret L.; Roschelle, Jeremy
Mathematics teacher educators are faced with designing teacher in-service professional development experiences for developing and transforming Technological Pedagogical Content Knowledge (TPACK) towards integrating digital technologies as mathematics learning tools. Online environments provide opportunities to a broad range of teachers, yet, the asynchronous nature presents communication and collaboration challenges. A researcher-conjectured, empirically-supported learning trajectory guides this online TPACK program for engaging teachers in knowledge-building communities. Three online technology education courses provide teachers with experiences as students, learning about the technologies while confronting challenges to their thinking about teaching with the technologies. The fourth course provides the teachers with key experiences through blended instruction. Through online explorations and discourses in their communities, they examine reform-based instructional strategies for teaching with technologies. Concurrently, they design, implement, analyze and reflect on their teaching experiences through their designed five-day unit in their mathematics classrooms. Four TPACK components reveal how this experience in knowledge-building communities transforms their TPACK.
Generalizability of a Technology-Based Intervention to Enhance Conceptual Understanding in Mathematics
(SRI International, 2018-11) Roschelle, Jeremy; Tipton, Elizabeth; Shechtman, Nicole; Vahey, Philip
Three previously reported experiments found that a technology-enhanced intervention increased student conceptual understanding of mathematics in Texas. To investigate generalizability to broader populations and settings, we triangulate among three methods. First, we examine interactions between demographic variables and intervention effects. We found that the intervention was not sensitive to typical variations in school populations. Second, we use propensity score methods to measure the match between the sample and a broader population. The sample matches the school population in Texas, with minor exceptions; we report adjusted effect sizes. Third, quasi-experimental research with populations outside of Texas are considered. Results from Florida and England were consistent with Texas findings. Across three methods, the results suggest that the experimental findings generalize across populations and settings. This work also establishes a practical approach to investigating generalizability in experimental research in schools.
Continuous Improvement and Postsecondary Student Success
(Digital Promise Global, 2019-03) Means, Barbara; Neisler, Julie
Review of our prior research on the effectiveness of adaptive courseware for Gates Foundation used in introducing our role to colleges participating in Every Learner Everywhere.
How Rule Induction Data Mining Can and Cannot Be Useful for Education Research
(AERA Online Paper Repository, 2019-04) Iwatani, Emi
This paper shares insights and recommendations on how rule-induction data mining can and cannot be useful to education research, based on re-analyzing two regression studies with rule induction approaches. Processes and findings were compared to identify whether, in what ways, and why rule-induction could add value. I found that rule-based approaches can provide unique descriptions of the sample that shows at-a-glance, how key predictors relate to each other and to the outcome. They can also identify relationships between variables that held for some subgroups but not others. It was important to clearly understand the difference between mining rules and mining rulesets, as well as the unique research questions that these answer, so that they complement rather than replacement regression.
An Efficacy Study of a Digital Core Curriculum for Grade 5 Mathematics
(AERA Open, 2019-05) Roschelle, Jeremy; Shechtman, Nicole; Feng, Mingyu; Singleton, Corinne
The Math Curriculum Impact Study was a large-scale randomized controlled trial (RCT) to test the efficacy of a digital core curriculum for Grade 5 mathematics. Reasoning Mind’s Grade 5 Common Core Curriculum was a comprehensive, adaptive, blended learning approach that schools in the treatment group implemented for an entire school year. Schools in the control group implemented their business-as-usual mathematics curriculum. The study was completed in 46 schools throughout West Virginia, resulting in achievement data from 1,919 students. It also included exploratory investigations of teacher practice and student engagement. The main experimental finding was a null result; achievement was similar in both experimental groups. The exploratory investigations help clarify interpretation of this result. As educational leaders throughout the United States adopt digital mathematics curricula and adaptive, blended approaches, our findings provide a relevant caution. However, our findings are not generalizable to all digital offerings, and there is a continuing need for refined theory, study of implementation, and rigorous experimentation to advise schools.
Commentary on Interest-Driven Creator theory: a US perspective on fostering interest, creativity, and habit in school
(Springer Open, 2019-10-25) Roschelle, Jeremy; Burke, Quinn
In this commentary on Interest-Driven Creator (IDC) theory, the authors reflect on the proposed three-step cycles of (i) sparking students’ interest, (ii) fostering individual creativity, and (iii) inculcating lifelong learning habits. Each component of IDC theory pulls together a wide span of prior research and emphasizes active roles for students. Although the context of IDC as a prototype for educational reform is K- 12 Asian classrooms, we note that some US schools are also mired in a focus on test scores. This is especially true among the US most struggling, low-income schools, where a lack of electives and afterschool programs correspond to diminished student perceptions about their own autonomy as learners and their future creative potential. Thus, while IDC is an important provocation for curricular reform in Asia, there is also the need to broaden its scope and begin to explore the potential of IDC as a leadership tool beyond Asia. The wider learning sciences community, the commentary concludes, is uniquely suited to support such an extension, and there are many opportunities for productive international collaboration.
Rubrics for Examining Deeper Learning in Middle School Science Classrooms
(Digital Promise, 2020-01) Iwatani, Emi; Means, Barbara; Romero, Maria R.
This classroom observation tool was developed for the Challenge Based Science Learning project funded by the William and Flora Hewlett Foundation, which brought together Challenge Based Learning and the Next Generation Science Standards (https://digitalpromise.org/initiative/next-generation-science/cbl-ngss/ ). It’s aligned to the activities and student work rubrics used in the project, can be used by researchers, educators and professional learning experts to study deeper learning in middle school science and engineering classrooms.
Deepening Science Engagement With Challenge Based Learning: Research Report
(Digital Promise, 2020-02) Iwatani, Emi; Means, Barbara; Romero, Maria R.; Vang, Mai Chou
Learn about the Challenge Based Science Learning Project and its larger implications for the fields of Next Generation Science Learning and Open Educational Resources. The project involved 18 middle school teachers and five administrators from three U.S. school districts partnering with instructional coaches and learning sciences researchers from Digital Promise to address an ambitious educational challenge: How might we deepen engagement and learning of middle school science in our schools and beyond?
Every Learner Everywhere and Lighthouse Institutions: First-Year Experiences
(Digital Promise and Every Learner Everywhere, 2020-03) Digital Promise; Every Learner Everywhere
In this report, Every Learner Everywhere & Lighthouse Institutions share first-year experiences of 2- and 4-year colleges piloting new versions of gateway courses incorporating adaptive learning in an effort to address achievement gaps for first-generation students, low-income students, and students of color by improving teaching and learning with the support of adaptive tools.
Digital Promise COVID-19 Student Survey Topline Data Report
(Digital Promise, 2020-07) Digital Promise and Langer Research Associates
This Digital Promise survey was conducted May 13-June 1, 2020, among a random national sample of 1,008 full- or part-time students enrolled in a two- or four-year college or university who were taking in-person or blended for-credit courses before the coronavirus outbreak began that then transitioned to remote instruction. The sample includes 620 students who took a STEM course that transitioned completely online. Results have a margin of sampling error of 3.6 points for the full sample, 4.6 points among students who took a STEM course and 5.8 points among those who did not take a STEM course. Error margins are larger for subgroups. At a 50/50 division of opinion, a difference of 8 points between STEM and non-STEM students is needed for significance at the 95 percent confidence level.
Suddenly Online: A National Survey of Undergraduates During the COVID-19 Pandemic
(Digital Promise, 2020-07) Means, Barbara; Neisler, Julie; Langer Research Associates
Digital Promise and Langer Research Associates developed the “Survey of Student Perceptions of Remote Teaching and Learning” to capture the experiences of undergraduates taking courses that transitioned to online instruction in response to the COVID-19 pandemic. The survey explores the nature of college courses as they were taught during the COVID-19 outbreak, the pervasiveness of various challenges undergraduates faced after the transition to remote instruction, and course features associated with higher levels of student satisfaction. Data analyses compared experiences of students from low-income, underrepresented, or rural backgrounds to those of students with none of these characteristics.This survey was administered in the spring of 2020 to a random national sample of 1,008 undergraduates, age 18 and older, who were taking college courses for credit that included in-person class sessions when the COVID-19 pandemic hit and had to finish the course by learning at a distance.
Unmasking Inequality: STEM Course Experience During the COVID-19 Pandemic
(Digital Promise, 2020-08) Means, Barbara; Neisler, Julie
This report describes the experiences of over 600 undergraduates who were taking STEM courses with in-person class meetings that had to shift to remote instruction in spring 2020 because of COVID-19. Internet connectivity issues were serious enough to interfere with students’ ability to attend or participate in their STEM course at least occasionally for 46% of students, with 15% of students experiencing such problems often or very often. A large majority of survey respondents reported some difficulty with staying motivated to work on their STEM courses after they moved online, with 45% characterizing motivation as a major problem. A majority of STEM students also reported having problems knowing where to get help with the course content after it went online, finding a quiet place to work on the course, and fitting the course in with other family or home responsibilities. Overall, students who reported experiencing a greater number of major challenges with continuing their course after it went online expressed lower levels of satisfaction with their course after COVID-19. An exception to this general pattern, though, was found for students from minoritized race/ethnicity groups, females, and lower-income students. Despite experiencing more challenges than other students did with respect to continuing their STEM courses remotely, these students were more likely to rate the quality of their experiences when their STEM course was online as just as good as, or even better than, when the course was meeting in person.
Ambitious Mashups: Reflections on a Decade of Cyberlearning Research
(Digital Promise, 2020-09) Center for Innovative Research in Cyberlearning
This report reflects on progress from over eight years of research projects in the cyberlearning community. The community involved computer scientists and learning scientists who received NSF awards to investigate the design of more equitable learning experiences with emerging technology—focusing on developing the learning theories and technologies that are likely to become important within 5-10 years. In early 2020, the Center for Innovative Research in Cyberlearning's team analyzed the portfolio of past and current projects in this community, and convened a panel of experts to reflect on important trends and issues, including artificial intelligence and learning; learning theories; research methods; out-of-school-time learning; and trends at NSF and beyond.
Implementing and scaling Differentiated Literacy System: A case of evaluators’ voices channeling outside-in and bottom-up perspectives for equity and continuous improvement
(Digital Promise, 2020-10) Vang, Mai Chou; Kasad, Zareen; Young, Viki M.
Digital Promise is supporting the implementation and scaling of Differentiated Literacy System (DLS), a tool and instructional coaching for K–3 teachers to meet individual students’ literacy needs. Based on interviews with over 200 teachers, principals, and district leaders, we bring forward our voices as evaluators by highlighting uncomfortable truths that lie at the crux of DLS’ desired impact on education. We place primacy on teachers’ “bottom-up” perspectives as those charged with creating instructional change. With a continuous improvement stance, we go beyond reporting findings to draw clear and actionable implications for DLS. The summary below presents key findings and recommendations that focus on organizational policies, structures, and practices to better support DLS and improve consistency in service quality for schools.
AI and the Future of Learning: Expert Panel Report
(Digital Promise, 2020-11) Roschelle, Jeremy; Lester, James; Fusco, Judi
This report is based on the discussion that emerged from a convening of a panel of 22 experts in artificial intelligence (AI) and in learning. It introduces three layers that can frame the meaning of AI for educators. First, AI can be seen as “computational intelligence” and capability can be brought to bear on educational challenges as an additional resource to an educator’s abilities and strengths. Second, AI brings specific, exciting new capabilities to computing, including sensing, recognizing patterns, representing knowledge, making and acting on plans, and supporting naturalistic interactions with people. Third, AI can be used as a toolkit to enable us to imagine, study, and discuss futures for learning that don’t exist today. Experts voiced the opinion that the most impactful uses of AI in education have not yet been invented. The report enumerates important strengths and weaknesses of AI, as well as the respective opportunities and barriers to applying AI to learning. Through discussions among experts about these layers, we observed new design concepts for using AI in learning. The panel also made seven recommendations for future research priorities.

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