Improvement Research Carried Out Through Networked Communities: Accelerating Learning about Practices that Support More Productive Student Mindsets

The research on academic mindsets shows significant promise for addressing important problems facing educators. However, the history of educational reform is replete with good ideas for improvement that fail to realize the promises that accompany their introduction. As a field, we are quick to implement new ideas but slow to learn how to execute well on them. If we continue to implement reform as we always have, we will continue to get what we have always gotten. Accelerating the field's capacity to learn in and through practice to improve is one key to transforming the good ideas discussed at the White House meeting into tools, interventions, and professional development initiatives that achieve effectiveness reliably at scale. Toward this end, this paper discusses the function of networked communities engaged in improvement research and illustrates the application of these ideas in promoting greater student success in community colleges. Specifically, this white paper:* Introduces improvement research and networked communities as ideas that we believe can enhance educators' capacities to advance positive change. * Explains why improvement research requires a different kind of measures -- what we call practical measurement -- that are distinct from those commonly used by schools for accountability or by researchers for theory development.* Illustrates through a case study how systematic improvement work to promote student mindsets can be carried out. The case is based on the Carnegie Foundation's effort to address the poor success rates for students in developmental math at community colleges.Specifically, this case details:- How a practical theory and set of practical measures were created to assess the causes of "productive persistence" -- the set of "non-cognitive factors" thought to powerfully affect community college student success. In doing this work, a broad set of potential factors was distilled into a digestible framework that was useful topractitioners working with researchers, and a large set of potential measures was reduced to a practical (3-minute) set of assessments.- How these measures were used by researchers and practitioners for practical purposes -- specifically, to assess changes, predict which students were at-risk for course failure, and set priorities for improvement work.-How we organized researchersto work with practitioners to accelerate field-based experimentation on everyday practices that promote academic mindsets(what we call alpha labs), and how we organized practitioners to work with researchers to test, revise, refine, and iteratively improve their everyday practices (using plando-study-act cycles).While significant progress has already occurred, robust, practical, reliable efforts to improve students' mindsets remains at an early formative stage. We hope the ideas presented here are an instructive starting point for new efforts that might attempt to address other problems facing educators, most notably issues of inequality and underperformance in K-12 settings

Quality Assessment for E-learning: a Benchmarking Approach (Third edition)

The primary purpose of this manual is to provide a set of benchmarks, quality criteria and notes for guidance against which e-learning programmes and their support systems may be judged. The manual should therefore be seen primarily as a reference tool for the assessment or review of e-learning programmes and the systems which support them. However, the manual should also prove to be useful to staff in institutions concerned with the design, development, teaching, assessment and support of e-learning programmes. It is hoped that course developers, teachers and other stakeholders will see the manual as a useful development and/or improvement tool for incorporation in their own institutional systems of monitoring, evaluation and enhancement

Factors Contributing to Student Retention in Online Learning and Recommended Strategies for Improvement: A Systematic Literature Review

Aim/Purpose This systematic literature review investigates the underlying factors that influence the gap between the popularity of online learning and its completion rate. The review scope within this paper includes an observation of possible causal aspects within the non-ideal completion rates in online learning environments and an identification of recommended strategies to increase retention rates. Background While online learning is increasingly popular, and the number of online students is steadily growing, student retention rates are significantly lower than those in the traditional environment. Despite the multitude of studies, many institutions are still searching for solutions for this matter. Methodology A systematic literature review was conducted on 40 studies published between 2010 and 2018. We established a set of criteria to guide the selection of eligible articles including topic relevance (aligned with the research questions), empirical studies, and publication time frame. Further steps were performed through a major database searching, abstract screening, full-text analysis, and synthesis process. Contribution This study adds to expanding literature regarding student retention and strategies in online learning environments within the higher education setting. Findings Revealed factors include institutional support, the level difficulty of the programs, promotion of a sense of belonging, facilitation of learning, course design, student behavioral characteristics, and demographic variables along with other personal variables. The recommended strategies identified for improving student retention are early interventions, at-all-times supports for students, effective communication, support for faculty teaching online classes, high-quality instructional feedback and strategies, guidance to foster positive behavioral characteristics, and collaboration among stakeholders to support online students. Recommendations for Practitioners Since factors within the open systems of online learning are interrelated, we recommend a collective effort from multiple stakeholders when addressing retention issues in online learning. Recommendation for Researchers We recommend that fellow scholars consider focusing on each influential factor and recommendation in regard to student retention in online learning environments as synthesized in this study. Findings will further enrich the literature on student retention in online learning environments. Future Research Future research may investigate various data-mining and analytics techniques pertaining to detection and prediction of at-risk students, the efficacy of student support and faculty support programs, and ways to encourage struggling students to adopt effective strategies that potentially engender positive learning behaviors

Categorization of Learning Analytics Models: Brief Literature Review

Learning analytics is one of the technological tools aiming to investigate educational database collected during the learning delivery process for further purpose of use in decision making or process update. Various types of methods on learning analytics are originated by scholars with their own ambition to contribute the field study. It is emerging study field since 2010s. This paper review literature papers which focused on categorization of learning analytics models with focus of its’ criteria. The papers are chosen from open scholar databases. The selected papers reviewed learning analytics model related studies to bring up their suggested categorization. The category based on the learning analytics models’ main objective as well as used approach. It is observed that prediction of student achievement or success is significant method among learning analytics models

The evolving field of learning analytics research in higher education: From data analysis to theory generation, an agenda for future research

Over the last decade the deployment and use of learning analytics has become routine in many universities around the world. The ability to analyse the way students interact with technology has demonstrated significant value for providing insights into student learning and there are now a wide range of uses for learning analytics in education. From use as a diagnostic tool, to a method for prediction, learning analytics in higher education has an emphasis on a wide range of outcome measures, including student retention, progression, attainment, performance, mastery, employability and engagement. In exploring how learning analytics can improve learning practice by transforming the ways we support learning processes, this editorial highlights some of the learning analytics research that has been published in AJET to date