Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

A Qualitative Study of Student-Centered Learning Practices in New England High Schools

In early 2015, the Nellie Mae Education Foundation (NMEF) contracted with the UMass Donahue Institute (UMDI) to conduct a qualitative study examining the implementation of student-centered learning (SCL) practices in select public high schools in New England. This study extends lines of inquiry explored through a prior (2014) project that UMDI conducted for NMEF. The 2014 study employed survey methodology to examine the prevalence of student-centered practices in public high schools across New England. The present study builds upon the investigation, using a variety of qualitative methods to further probe the richness and complexity of SCL approaches in use across the region. Specifically, this study was designed to address what student-centered practices "look like" in an array of contexts. The study also addresses the perceived impacts that SCL approaches have on students, staff, and schools. Additionally, it highlights the broad array of factors within and beyond school walls that reportedly foster and challenge the implementation of SCL practices. This study seeks to help NMEF understand the intricacies of SCL and provides strategic considerations for how Nellie Mae can promote the adoption and development of student-centered practices in the region.Nellie Mae organizes student-centered learning by four tenets: (1) learning is personalized; (2) learning is competency-based; (3) learning takes place anytime, anywhere; and (4) students take ownership.Specifically, the study addresses five research questions:What are the characteristics of student-centered practices in relation to the four SCL tenets? How are SCL approaches implemented?What are the salient contextual factors (e.g., systems, structures, policies, procedures) associated with the implementation of SCL practices? How do they support, impede, and otherwise shape the adoption, development, and implementation of SCL approaches?How are schools with moderate and high levels of SCL implementation organized to foster SCL practices? What mechanisms are in place to promote student-centered learning?What is the role of SCL approaches in schools and classrooms? In what ways, if at all, are they embedded in the goals and practices of schools and classrooms?What is the quality of SCL instructional practices in study schools? What relationships, if any, do administrators and educators perceive between these approaches and student learning

Maximizing Competency Education and Blended Learning: Insights from Experts

In May 2014, CompetencyWorks brought together twenty-three technical assistance providers to examine their catalytic role in implementing next generation learning models, share each other's knowledge and expertise about blended learning and competency education, and discuss next steps to move the field forward with a focus on equity and quality. Our strategy maintains that by building the knowledge and networks of technical assistance providers, these groups can play an even more catalytic role in advancing the field. The objective of the convening was to help educate and level set the understanding of competency education and its design elements, as well as to build knowledge about using blended learning modalities within competency-based environments. This paper attempts to draw together the wide-ranging conversations from the convening to provide background knowledge for educators to understand what it will take to transform from traditional to personalized, competency-based systems that take full advantage of blended learning

Promising State Policies for Personalized Learning

This report is a valuable resource for state policymakers—whether they are seeking to create conditions in state policy to support personalized learning, moving forward with initiatives to develop personalized learning pilot programs, hosting task forces to explore policy issues and needs, or taking a comprehensive policy approach for supporting advanced personalized learning models.Personalized learning is where instruction is tailored to each student's strengths, needs, and interests—including enabling student voice and choice in what, how, when, and where they learn—to provide flexibility and supports to ensure mastery of the highest standards possible

When Failure Is Not an Option: Designing Competency-Based Pathways for Next Generation Learning

Proposes an online learning-assisted model in which students advance by demonstrating mastery of subjects based on clear, measurable objectives and meaningful assessments. Examines innovation drivers, challenges, and philanthropic opportunities

Neurofeedback-Based Moral Enhancement and the Notion of Morality

Some skeptics question the very possibility of moral bioenhancement by arguing that if we lack a widely acceptable notion of morality, we will not be able to accept the use of a biotechnological technique as a tool for moral bioenhancement. I will examine this skepticism and argue that the assessment of moral bioenhancement does not require such a notion of morality. In particular, I will demonstrate that this skepticism can be neutralized in the case of recent neurofeedback techniques. This goal will be accomplished in four steps. First, I will draw an outline of the skepticism against the possibility of moral bioenhancement and point out that a long-lasting dispute among moral philosophers nourishes this skepticism. Second, I will survey recent neurofeedback techniques and outline their three features: the variety of the target human faculties, such as emotion, cognition, and behavior; the flexibility or personalizability of the target brain state; and the nonclinical application of neurofeedback techniques. Third, I will argue that, by virtue of these three unique features, neurofeedback techniques can be a tool for moral bioenhancement without adopting any specific notion of morality. Fourth, I will examine the advantages and threats that neurofeedback-based moral enhancement may have. Finally, I will conclude that neurofeedback-based moral enhancement can become a new and promising tool for moral bioenhancement and requires further ethical investigations on its unique features

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i.e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-of-the-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers. In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns. (2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on state-of-the-art decision rules from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019