With cross-disciplinary academic interests increasing and advising resources over capacity, the importance of exploring data-assisted methods to support student decision making has never been higher. We build on findings methodologies a quickly developing literature around prediction recommendation in higher education develop novel recurrent neural network-based system for suggesting courses help students prepare target interest, personalized their estimated prior knowledge background zone...

Collaborative filtering based algorithms, including Recurrent Neural Networks (RNN), tend towards predicting a perpetuation of past observed behavior. In recommendation context, this can lead to an overly narrow set suggestions lacking in serendipity and inadvertently placing the user what is known as "filter bubble." paper, we grapple with issue filter bubble context course system production at public university. Our approach present results that are novel or unexpected student but still...

Equity of educational outcome and fairness AI with respect to race have been topics increasing importance in education. In this work, we address both empirical evaluations grade prediction higher education, an important task improve curriculum design, plan interventions for academic support, offer course guidance students. With as the aim, trial several strategies label instance balancing attempt minimize differences algorithm performance race. We find that adversarial learning approach,...

Learners are often faced with the following scenario: given a goal for future, and what they have learned in past, should do now to best achieve their goal? We build on work utilizing deep learning make inferences about how past actions correspond future outcomes enhance this novel application of backpropagation learn per-user optimized next actions. apply technique two datasets, one from university setting which courses can be recommended towards preparation target course, massive open...

Cognitive diagnosis, a fundamental task in education area, aims at providing an approach to reveal the proficiency level of students on knowledge concepts. Actually, monotonicity is one basic conditions cognitive diagnosis theory, which assumes that student's monotonic with probability giving right response test item. However, few previous methods consider during optimization. To this end, we propose Item Response Ranking framework (IRR), aiming introducing pairwise learning into well model...

Recommender systems are widely used by platforms/merchants to find the products that likely interest consumers. However, existing dynamic methods still face challenges with regard diverse behaviors, variability in shifts, and identification of psychological dynamics. Premised on marketing funnel perspective analyze consumer shopping journeys, this study proposes a novel effective machine learning approach for product recommendation, namely, multi-stage Bayesian network (MS-DBN), which models...

Collaborative filtering based algorithms, including Recurrent Neural Networks (RNN), tend towards predicting a perpetuation of past observed behavior. In recommendation context, this can lead to an overly narrow set suggestions lacking in serendipity and inadvertently placing the user what is known as "filter bubble." paper, we grapple with issue filter bubble context course system production at public university. Most universities United States encourage students explore developing...

In the field of deep learning, response selection is key to retrieval-based chatbots. Faced with challenge contextual meaning comprehension and semantic matching, we propose matching a through diverse information by stacked multi-head attention. First, construct multi-granular representations on embedded input sentences Based these representations, build two different matrices for context-response segment pairs another attention stack. Next, use two-layer CNN extract hidden from matrices....

With the rapid development of GIS technology, increasingly platforms have provided various technologies data management, thematic modeling, spatial analysis methods, processes and results representation, to ecological environment sensitivity analysis. Methodologies such as theory study, case combination qualitative quantitative are used in this study. First all, paper introduces related concept research ecological-sensitivity GIS. Then , procedures evaluation set up, combining with one. And...

With cross-disciplinary academic interests increasing and advising resources over capacity, the importance of exploring data-assisted methods to support student decision making has never been higher. We build on findings methodologies a quickly developing literature around prediction recommendation in higher education develop novel recurrent neural network-based system for suggesting courses help students prepare target interest, personalized their estimated prior knowledge background zone...

The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact. In this paper, we demonstrate novel ways in synthesis these data illuminate terrain users' environment and support them their decision making wayfinding. A application Recurrent Neural Networks skip-gram models, approaches popularized by to modeling language, are brought bear on student university enrollment sequences create vector representations courses map out...