401,287 research outputs found
Applicability of the technology acceptance model for widget-based personal learning environments
This contribution presents results from two exploratory studies on technology acceptance and use of widget-based personal learning environments. Methodologically, the investigation carried out applies the unified theory of acceptance and use of technology (UTAUT). With the help of this instrument, the study assesses expert judgments about intentions to use and actual use of the emerging technology of flexibly arranged combinations of use-case-sized mini learning tools. This study aims to explore the applicability of the UTAUT model and questionnaire for widget-based personal learning environments and reports back on the experiences gained with the two studies
A Survey on Bayesian Deep Learning
A comprehensive artificial intelligence system needs to not only perceive the
environment with different `senses' (e.g., seeing and hearing) but also infer
the world's conditional (or even causal) relations and corresponding
uncertainty. The past decade has seen major advances in many perception tasks
such as visual object recognition and speech recognition using deep learning
models. For higher-level inference, however, probabilistic graphical models
with their Bayesian nature are still more powerful and flexible. In recent
years, Bayesian deep learning has emerged as a unified probabilistic framework
to tightly integrate deep learning and Bayesian models. In this general
framework, the perception of text or images using deep learning can boost the
performance of higher-level inference and in turn, the feedback from the
inference process is able to enhance the perception of text or images. This
survey provides a comprehensive introduction to Bayesian deep learning and
reviews its recent applications on recommender systems, topic models, control,
etc. Besides, we also discuss the relationship and differences between Bayesian
deep learning and other related topics such as Bayesian treatment of neural
networks.Comment: To appear in ACM Computing Surveys (CSUR) 202
Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling
Adaptive learning technology solutions often use a learner model to trace
learning and make pedagogical decisions. The present research introduces a
formalized methodology for specifying learner models, Logistic Knowledge
Tracing (LKT), that consolidates many extant learner modeling methods. The
strength of LKT is the specification of a symbolic notation system for
alternative logistic regression models that is powerful enough to specify many
extant models in the literature and many new models. To demonstrate the
generality of LKT, we fit 12 models, some variants of well-known models and
some newly devised, to 6 learning technology datasets. The results indicated
that no single learner model was best in all cases, further justifying a broad
approach that considers multiple learner model features and the learning
context. The models presented here avoid student-level fixed parameters to
increase generalizability. We also introduce features to stand in for these
intercepts. We argue that to be maximally applicable, a learner model needs to
adapt to student differences, rather than needing to be pre-parameterized with
the level of each student's ability
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Recommended from our members
Creating awareness of kinaesthetic learning using the Experience API: current practices, emerging challenges, possible solutions
We describe our use of the Experience API in preparing blue-collar workers for three frequently arising work contexts, including, for example, the requirement to perform maintenance tasks exactly as specified, consistently, quickly, and without error. We provide some theoretical underpinning for modifying and updating the API to remain useful in near-future training scenarios, such as having a shorter time allowed for kinaesthetic learning experiences than in traditional apprenticeships or training. We propose ways to involve a wide range of stakeholders in appraising the API and ensuring that any enhancements to it, or add-ons, are useful, feasible and compatible with current TEL practices and tools, such as learning-design modelling languages
Hierarchical Attention Network for Visually-aware Food Recommendation
Food recommender systems play an important role in assisting users to
identify the desired food to eat. Deciding what food to eat is a complex and
multi-faceted process, which is influenced by many factors such as the
ingredients, appearance of the recipe, the user's personal preference on food,
and various contexts like what had been eaten in the past meals. In this work,
we formulate the food recommendation problem as predicting user preference on
recipes based on three key factors that determine a user's choice on food,
namely, 1) the user's (and other users') history; 2) the ingredients of a
recipe; and 3) the descriptive image of a recipe. To address this challenging
problem, we develop a dedicated neural network based solution Hierarchical
Attention based Food Recommendation (HAFR) which is capable of: 1) capturing
the collaborative filtering effect like what similar users tend to eat; 2)
inferring a user's preference at the ingredient level; and 3) learning user
preference from the recipe's visual images. To evaluate our proposed method, we
construct a large-scale dataset consisting of millions of ratings from
AllRecipes.com. Extensive experiments show that our method outperforms several
competing recommender solutions like Factorization Machine and Visual Bayesian
Personalized Ranking with an average improvement of 12%, offering promising
results in predicting user preference for food. Codes and dataset will be
released upon acceptance
- …