Integrated e-Learning Modules for Developing an Entrepreneurial Mindset: Direct Assessment of Student Learning

In an effort to develop an entrepreneurial mindset in all our engineering and computer science students, the University of New Haven is embedding entrepreneurial concepts throughout the 4-year curricula in their majors. This is done with the use of several short e-learning modules developed by content experts. The modules are integrated into engineering and computer science courses by faculty who reinforce concepts through a related activity, project, or assignment. The e-learning modules, available online through course management systems, are self-paced and targeted at conceptual learning of 18 specific entrepreneurial topics. Using a flipped-classroom instructional model, students complete the modules outside of class, typically over a set two-week period, and instructors engage the students in discussion either in-class or online and through an activity. This mode of integration enables the assessment of higher cognitive understanding of the concepts and students’ ability to apply what they learn. At present, 12 modules have been developed. In addition to the modules being integrated within the University, they have also been adopted by faculty at 42 other institutions across the country over the past three years. The broad-scale deployment has provided assessment and feedback data regarding the effectiveness of integrating the modules into existing courses using a blended approach (face-to-face and online learning). Whereas prior work relied on indirect assessment using pre/post student surveys to quantify the acquisition of knowledge from the e-learning modules and contextual activities, the current work employs student deliverables that are directly assessed by instructors. Faculty were provided assessment rubrics based on criteria aligned with the learning outcomes of the e-learning modules. Direct assessment is tangible, visible and measurable, and provides more compelling evidence of student learning. In this paper we propose an Entrepreneurial Mindset Learning Index to map and quantify the progress of students toward attaining an entrepreneurial mindset. The criteria in the assessment rubrics for the e-learning modules were mapped to the learning outcomes associated with an entrepreneurial mindset proposed by the Kern Entrepreneurial Engineering Network’s (KEEN) framework. The KEEN framework is based on the premise that an entrepreneurial mindset is characterized by a persistent curiosity of all things, the skills to make connections between seemingly unrelated things, and an ever-present goal to create value. Through the mapping, the direct assessment results provided an indication of how well students taking courses with integrated e-learning modules achieved elements of an entrepreneurial mindset

Learning Culture and Innovative Work Behaviour: Does Attitude Toward Change Matter?

Purpose:  This empirical research aimed to analyze the mediating role of attitude toward change (ATC) on the relationship between the learning organization dimensions (LODs) and innovative work behaviour (IWB) among academics with the rank of professor in Malaysian public higher education institutions (HEIs).   Theoretical framework: Organizational effectiveness, leading to sustainable goal development to happen swiftly, requires an attitude toward change (ATC) that leads to IWB among the institutional members who are undoubtedly already in a learning culture with supporting milieus.   Design/methodology/approach: 366 valid survey responses were collected using self-administered questionnaires delivered through the online survey with professors serving Malaysian public HEIs as target respondents. The study used Partial Least Square Structural Equation Modeling (PLS‐SEM) for exploratory data analysis and hypothesis testing.   Findings:  The results indicate that four LODs, systems connection, strategic leadership, continuous learning and dialogue and inquiry, significantly affect IWB in the presence of ATC as a mediator. On the other hand, ATC cannot mediate the outcome of IWB substantially, testing for the following exogenous variables: team level of learning, and two different dimensions of learning at the organization level, embedded system and empowerment.   Research, Practical & Social implications:  This study can be instructive for HEIs policymakers and those charged with institutional change and development. The study found that the HEIs’ effort in creating dynamic continuous learning opportunities and endless action in creating a culture of enthusiasm for questioning, timely feedback, and research enhanced the IWB through the upshot of ATC. At the structural level,  encouraging active participation and contributions to global intelligence and learning-oriented leadership marks the progress of the IWB. The capacity of the HEIs to integrate people and structures ultimately reaches healthier work behaviour necessary for the present needs of creative educational institutions. Practical implications and future research opportunities are in this empirical paper.   Originality/value:  The study empirically supports the strength of measures used to gauge the LODs, ATC and IWB. The mediating effects of ATC are substantial, with solid research evidence to suggest it is essential to identify what influences employee attitudes to change to facilitate and optimize employee receptiveness to change and, therefore, the likely success of HEIs change.

Toward an Ecology of Gaming

In her introduction to the Ecology of Games, Salen argues for the need for an increasingly complex and informed awareness of the meaning, significance, and practicalities of games in young people's lives. The language of the media is replete with references to the devil (and heavy metal) when it comes to the ill-found virtues of videogames, while a growing movement in K-12 education casts them as a Holy Grail in the uphill battle to keep kids learning. Her essay explores the different ways the volume's contributors add shades of grey to this often black-and-white mix, pointing toward a more sophisticated understanding of the myriad ways in which gaming could and should matter to those considering the future of learning

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

Cracking the Code: Synchronizing Policy and Practice for Performance-Based Learning

Proposes a policy framework for integrating performance-based learning into the education system, synchronizing policy and practice, and ensuring collaborative state leadership and flexible federal leadership. Lists state policy issues and exemplars

Smart School Budgeting: Resources for Districts

In an era of aggressive public education reform, school districts face increasing pressure to produce higher levels of student performance with increasingly limited resources. The economic downturn has forced many districts to tighten their belts, and careful thought must be given to how each and every dollar is spent. Optimally, district leaders should work with stakeholders in their communities to set goals, analyze current spending, provide transparency in their budgeting, and consider cost-saving and reallocation strategies. The Rennie Center has created a toolkit, Smart School Budgeting: Resources for Districts, aiming to assist district leaders in decision-making about school budgeting. Smart School Budgeting is intended to push school leaders to take a more deliberative approach to school budgeting. The resources presented in the toolkit act as a starting point for districts examining their own budgeting processes. The document is designed as a user-friendly summary of existing literature and tools on school finance, budgeting, and resource allocation that directs district leaders and school business officials to practical and useful information to shape resource decisions. Each section includes an overview of a critical topic in school budgeting, summaries of useful documents and resources, relevant case studies (if available), and a resource list with hyperlinked documents for easy access. The toolkit is organized around the following topics: introduction and context for school budget analysis; setting goals; types of budgets; strategies for analyzing spending; tools for budget analysis; and cost-saving strategies.This toolkit was released at a public event on October 3, 2012

A reinforcement learning theory for homeostatic regulation

Reinforcement learning models address animal’s behavioral adaptation to its changing “external” environment, and are based on the assumption that Pavlovian, habitual and goal-directed responses seek to maximize reward acquisition. Negative-feedback models of homeostatic regulation, on the other hand, are concerned with behavioral adaptation in response to the “internal” state of the animal, and assume that animals’ behavioral objective is to minimize deviations of some key physiological variables from their hypothetical setpoints. Building upon the drive-reduction theory of reward, we propose a new analytical framework that integrates learning and regulatory systems, such that the two seemingly unrelated objectives of reward maximization and physiological-stability prove to be identi- cal. The proposed theory shows behavioral adaptation to both internal and external states in a disciplined way. We further show that the proposed framework allows for a unified explanation of some behavioral pattern like motivational sensitivity of different associative learning mechanism, anticipatory responses, interaction among competing motivational systems, and risk aversion

Breakthroughs in Shared Measurement and Social Impact

A surprising new breakthrough is emerging in the social sector: A handful of innovative organizations have developed web-based systems for reporting the performance, measuring the outcomes, and coordinating the efforts of hundreds or even thousands of social enterprises within a field. These nascent efforts carry implications well beyond performance measurement, foreshadowing the possibility of profound changes in the vision and effectiveness of the entire nonprofit sector. This paper, based on six months of interviews and research by FSG Social Impact Advisors, examines twenty efforts to develop shared approaches to performance, outcome, or impact measurement across multiple organizations. The accompanying appendices include a short description of each system and four more in-depth case studies

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201