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Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can be designed to improve strategies and frustration can be minimized by adapting to a student's natural way of thinking rather than trying to fit a standard strategy for all. While it may be possible for human experts to identify strategies manually in classroom settings with sufficient student interaction, it is not possible to scale this up to big data. Therefore, we leverage advances in Machine Learning and AI methods to perform scalable strategy prediction that is also fair to students at all skill levels. Specifically, we develop an embedding called MVec where we learn a representation based on the mastery of students. We then cluster these embeddings with a non-parametric clustering method where we progressively learn clusters such that we group together instances that have approximately symmetrical strategies. The strategy prediction model is trained on instances sampled from these clusters. This ensures that we train the model over diverse strategies and also that strategies from a particular group do not bias the DNN model, thus allowing it to optimize its parameters over all groups. Using real world large-scale student interaction datasets from MATHia, we implement our approach using transformers and Node2Vec for learning the mastery embeddings and LSTMs for predicting strategies. We show that our approach can scale up to achieve high accuracy by training on a small sample of a large dataset and also has predictive equality, i.e., it can predict strategies equally well for learners at diverse skill levels. [For the complete proceedings, see ED630829.]

Predicting student problem-solving strategies is a complex problem but one that can significantly impact automated instruction systems since they can adapt or personalize the system to suit the learner. While for small datasets, learning experts may be able to manually analyze data to infer student strategies, for large datasets, this approach is infeasible. We develop a Machine Learning model to predict strategies from student data. While Deep Neural Network (DNN) based methods such as LSTMs can be applied for this task, they often have long convergence times for large datasets and like several other DNN-based methods have the inherent problem of overfitting the data. To address these issues, we develop a Neuro-symbolic approach for strategy prediction, namely a model that combines strengths of symbolic AI (that can encode domain knowledge) with DNNs. Specifically, we encode relationships in the data using Markov Logic and use symmetries among these relationships to train an LSTM more efficiently. In particular, we use an importance sampling approach where we sample the training data such that for clusters/groups of symmetrical instances (instances where the strategies are likely to be symmetric), we only pick representative samples for training the model instead of using the whole group. Further, since some groups may contain more diverse strategies than the others, we adapt the importance weights based on previously observed samples. Through empirical evaluation on the KDD EDM challenge datasets, we show the scalability of our approach. [For the full proceedings, see ED615472.]

This case study discusses the importance of food supplier selection and forecasting to minimize food cost and maximize profit for a hotel restaurant. The importance of data analytics in supplier selection is emphasized. The case provides students with a more comprehensive understanding of spoilage and the calculation of food cost percentage. This research uses a data driven problem solving approach to accurately forecast food requirements and minimize spoilage, which should save the hotel restaurant under study significant dollars, help the environment and strengthen the operation. The events described in this case are based on real world experiences. [ABSTRACT FROM AUTHOR]

Although groupware for scheduling and calendaring in corporate environments is a mature field of study, the requirements for such systems must be modified when applied to more diverse environments such as university campuses, or residential communities. The communities feature non-traditional events as well as intricate and loosely defined relationships between members. In this setting, groupware must not only facilitate communication and scheduling of events, but also build community and encourage additional interaction between community members. Such systems must seamless integrate into current interaction process, growing to meet the changing needs of the community, while always maintaining the privacy of individual members. CIRCA was developed as a prototype system to meet the requirements we describe in this paper and initial testing shows that our solutions do in fact address the issues effectively. [ABSTRACT FROM AUTHOR]

The article presents a study on the characterization of the electrospun web structure and tensile properties. It describes electrospinning as a versatile method for nanofiber production which can also be used in tissue engineering and filtration. It indicates the use of a mechanical model using the method of force analysis in the structural determination of electrospun web and stress-strain property. Results show the bonding of the fibers at the cross-over points to some extent during the electrospinning process.