A unique timely moment for embedding intelligence in applications

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The Learning Edge: Supporting Student Success in a Competency-Based Learning Environment

State by state, our country is revamping our education system to ensure that each and every one of our young people is college and career ready. Over two-thirds of our states have adopted policies that enable credits to be awarded based on proficiency in a subject, rather than the one-size-fits-all seat-time in a classroom. Now states such as Maine and New Hampshire are taking the next step in establishing competency based diplomas in which students are expected to demonstrate that they can apply their skills and knowledge. To ensure high-quality competency education, in 2011 one hundred innovators created a working definition to guide the field. This paper delves into the fourth element of the definition: Students receive timely, differentiated support based on their individual learning needs. Through a series of interviews and site visits, an understanding of how support in a competency-based school differs from traditional approaches emerged. Learning in a competency-based environment means pushing students and adults to the edge of their comfort zone and competence -- the learning edge. Common themes that were drawn from the wide variety of ways schools support students became the basis for the design principles introduced here. It is essential to pause and understand the importance of timely, differentiated support. Our commitment to prepare all of our young people for college and careers demands that we be intentional in designing schools to effectively meet the needs of students of all races, classes, and cultures. It also demands our vigilance in challenging inequity. There is a risk in competency education -- a risk that learning at one's own pace could become the new achievement gap and that learning anywhere/anytime could become the new opportunity gap. Therefore, our goal in writing this paper is to provide ideas and guidance so that innovators in competency education can put into place powerful systems of supports for students in order to eradicate, not replicate, the inequities and variability in quality and outcomes that exist in our current system. Please consider this paper as an initial exploration into what it means to provide support for the individual learning needs of students. It is designed to generate reflection, analysis, and feedback

Principles in Patterns (PiP) : Institutional Approaches to Curriculum Design Institutional Story

The principal outputs of the PiP Project surround the Course and Class Approval (C-CAP) system. This web-based system built on Microsoft SharePoint addresses and resolves many of the issues identified by the project. Generally well received by both academic and support staff, the system provides personalised views, adaptive forms and contextualised support for all phases of the approval process. Although the system deliberately encapsulates and facilitates existing approval processes thus achieving buy-in, it is already achieving significant improvements over the previous processes, not only in reducing the administrative overheads but also in supporting curriculum design and academic quality. The system is now embedded across three faculties and is now considered by the University of Strathclyde to be a "core institutional service". Alongside the C-CAP system the PiP Project also cultivated a suite of approaches: an incremental systems development methodology; a structured and replicable evaluation approach, and; Strathclyde's Lean Approach to Efficiencies in Education Kit (SLEEK) business process improvement methodology Each is based on recognised formal techniques, providing the basis for a rigorous approach. This is contextualised within and adapted to the HE institutional context thus building the foundation not only for the project but ultimately for institution wide process improvement. This "institutional story" report summarises the principal outcomes of the Project

Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data

The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users.Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network base-lines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques

Incorporating Learner Emotions through Sentiment Analysis in Adaptive E-learning Systems: A Pilot Study

This research delves into the exciting avenue of incorporating learner emotions into adaptive E-learning systems through sentiment analysis techniques. Utilizing a pilot study with 40 undergraduate computer science students, we investigated the ability of an adaptive system to detect boredom and frustration in learner forum posts and subsequently personalize content or offer support based on these emotional states. This approach proved demonstrably successful, as learners in the experimental group who received emotion-based adaptation exhibited both increased engagement (reflected in higher time spent on tasks) and improved learning outcomes (evidenced by higher post-test scores). Furthermore, qualitative feedback revealed positive responses to the personalized interventions, indicating that learners appreciated the tailored support provided by the system. While acknowledging limitations such as the small sample size and single subject area, this study firmly establishes the promising potential of emotion-aware adaptive systems. By addressing the emotional dynamics of the learning process, such systems can pave the way for truly personalized and responsive E-learning environments that cater to individual learner needs and foster deeper engagement, positive learning experiences, and ultimately, success for all students