140,099 research outputs found
Lessons Learned from Designing an Open-Source Automated Feedback System for STEM Education
As distance learning becomes increasingly important and artificial
intelligence tools continue to advance, automated systems for individual
learning have attracted significant attention. However, the scarcity of
open-source online tools that are capable of providing personalized feedback
has restricted the widespread implementation of research-based feedback
systems. In this work, we present RATsApp, an open-source automated feedback
system (AFS) that incorporates research-based features such as formative
feedback. The system focuses on core STEM competencies such as mathematical
competence, representational competence, and data literacy. It also allows
lecturers to monitor students' progress. We conducted a survey based on the
technology acceptance model (TAM2) among a set of students (N=64). Our findings
confirm the applicability of the TAM2 framework, revealing that factors such as
the relevance of the studies, output quality, and ease of use significantly
influence the perceived usefulness. We also found a linear relation between the
perceived usefulness and the intention to use, which in turn is a significant
predictor of the frequency of use. Moreover, the formative feedback feature of
RATsApp received positive feedback, indicating its potential as an educational
tool. Furthermore, as an open-source platform, RATsApp encourages public
contributions to its ongoing development, fostering a collaborative approach to
improve educational tools.Comment: 40 pages, 6 figure
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
A Socio-Informatic Approach to Automated Account Classification on Social Media
Automated accounts on social media have become increasingly problematic. We
propose a key feature in combination with existing methods to improve machine
learning algorithms for bot detection. We successfully improve classification
performance through including the proposed feature.Comment: International Conference on Social Media and Societ
Context-Aware Systems for Sequential Item Recommendation
Quizlet is the most popular online learning tool in the United States, and is
used by over 2/3 of high school students, and 1/2 of college students. With
more than 95% of Quizlet users reporting improved grades as a result, the
platform has become the de-facto tool used in millions of classrooms. In this
paper, we explore the task of recommending suitable content for a student to
study, given their prior interests, as well as what their peers are studying.
We propose a novel approach, i.e. Neural Educational Recommendation Engine
(NERE), to recommend educational content by leveraging student behaviors rather
than ratings. We have found that this approach better captures social factors
that are more aligned with learning. NERE is based on a recurrent neural
network that includes collaborative and content-based approaches for
recommendation, and takes into account any particular student's speed, mastery,
and experience to recommend the appropriate task. We train NERE by jointly
learning the user embeddings and content embeddings, and attempt to predict the
content embedding for the final timestamp. We also develop a confidence
estimator for our neural network, which is a crucial requirement for
productionizing this model. We apply NERE to Quizlet's proprietary dataset, and
present our results. We achieved an R^2 score of 0.81 in the content embedding
space, and a recall score of 54% on our 100 nearest neighbors. This vastly
exceeds the recall@100 score of 12% that a standard matrix-factorization
approach provides. We conclude with a discussion on how NERE will be deployed,
and position our work as one of the first educational recommender systems for
the K-12 space
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The OpenupEd quality label: benchmarks for MOOCs
In this paper we report on the development of the OpenupEd Quality Label, a self-assessment and review quality assurance process for the new European OpenupEd portal (www.openuped.eu) for MOOCs (massive open online courses). This process is focused on benchmark statements that seek to capture good practice, both at the level of the institution and at the level of individual courses. The benchmark statements for MOOCs are derived from benchmarks produced by the E xcellence e learning quality projects (E-xcellencelabel.eadtu.eu/). A process of self-assessment and review is intended to encourage quality enhancement, captured in an action plan. We suggest that a quality label for MOOCs will benefit all MOOC stakeholders
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