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

USING THE REVISED BLOOM\u27S TAXONOMY TO SCAFFOLD STUDENT LEARNING IN BUSINESS INTELLIGENCE/BUSINESS ANALYTICS

The paper aims to make a theoretical and practical contribution to the field of Business Intelligence/Business Analytics (BI) education, by addressing the following practice-inspired, teaching-related research question: How to design learning activities to scaffold student learning in Business Intelligence (Business Analytics) towards more advanced cognitive and knowledge dimensions, and along the way help students to further develop their meta-cognitive skills of learning how to learn The paper adopts the revised Bloom?s taxonomy as a theoretical framework and demonstrates its use in designing and implementation of BI-related learning activities at different levels of cognitive and knowledge dimensions. The paper also offers some research contributions related to the framework itself, in particular correlation of different levels of cognitive process and knowledge dimensions, not captured by the revised taxonomy

Architecture of Engagement: Autonomy-Supportive Leadership for Instructional Improvement

This multiple paper dissertation addresses the importance of improving student success in online higher education programs by providing support for instructors. The autonomy-supportive structures to improve instructional practice are explained through three main domains, including instructional development, instructional design, and instructional practice. The first paper addresses instructional leadership with the theoretical foundations and practical considerations necessary for instructional leaders. Recommendations are made to use microcredentials or digital badges to scaffold programming using self-determination theory. The second paper addresses the importance of instructional design in improving instructional practice including the intentionality involved in implementing a gamification strategy to improve online student motivation. The third paper addresses instructional practice with a mixed-method sequential explanatory case study. Using the community of inquiry framework, this paper explains intentional course design, course facilitation, and student perceptions of the digital powerups strategy. The conclusion considers implications for practice and the need for instructional leaders to scaffold an architecture of engagement to support instructors and improve student success

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Learning design – making practice explicit

New technologies have immense potential for learning, but the sheer variety possible also creates challenges for learners in terms of navigating through an increasingly complex digital landscape and for teachers in terms of how to design and support learning interventions. How can learners and teachers make informed decisions about what technologies to use in the design and support of learning activities? This presentation will consider this question and present a new methodology for design – 'learning design', which aims to shift the creation and support of learning from what has traditionally been an implicit, belief-based practice to one that is explicit and design based. Learning design research at the Open University, UK has included the development of a set of conceptual design views, a tool for visualising designs (CompendiumLD) and a social networking site, for sharing and discussing learning and teaching ideas and designs (Cloudworks). An overview of this work will be provided, along with a discussion of the perceived benefits of this new approach to educational design

Remote Experimentation supported by Learning Analytics and Recommender Systems

This paper proposes a process based on learning analytics and recommender systems targeted at making suggestions to students about their remote laboratories activities and providing insights to all stakeholders taking part in the learning process. To apply the process, a log with requests and responses of remote experiments from the VISIR project were analyzed. A request is the setup of the experiment including the assembled circuits and the configurations of the measuring equipment. In turn, a response is a message provided by the measurement server indicating measures or an error when it is not possible to execute the experiment. Along the two phases of analysis, the log was analyzed and summarized in order to provide insights about students' experiments. In addition, there is a recommendation service responsible for analyzing the requests thus returning, in case of error, precise information about the assembly of circuits or configurations. The evaluation of the process is consistent in what regards its ability to afford recommendations to the students as they carry out the experiments. Moreover, the summarized information intends to offer teachers means to better understand and develop strategies to scaffold students' learning.info:eu-repo/semantics/publishedVersio

Remote Experimentation supported by Learning Analytics and Recommender Systems

This paper proposes a process based on learning analytics and recommender systems targeted at making suggestions to students about their remote laboratories activities and providing insights to all stakeholders taking part in the learning process. To apply the process, a log with requests and responses of remote experiments from the VISIR project were analyzed. A request is the setup of the experiment including the assembled circuits and the configurations of the measuring equipment. In turn, a response is a message provided by the measurement server indicating measures or an error when it is not possible to execute the experiment. Along the two phases of analysis, the log was analyzed and summarized in order to provide insights about students' experiments. In addition, there is a recommendation service responsible for analyzing the requests thus returning, in case of error, precise information about the assembly of circuits or configurations. The evaluation of the process is consistent in what regards its ability to afford recommendations to the students as they carry out the experiments. Moreover, the summarized information intends to offer teachers means to better understand and develop strategies to scaffold students' learning.info:eu-repo/semantics/publishedVersio

Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study

Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal), relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI), detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below