Learning Activity Sequencing in Personalized Education System

Personalized education provides an open learning environment which enriches the advanced technologies to establish a paradigm shift, active and dynamic teaching and learning patterns. E-learning has a various established approaches to the creation and sequencing of content-based, single learner, and self-paced learning objects. However, there is little understanding of how to create sequences of learning activities which involve groups of learners interacting within a structured set of collaborative environments. In this paper, we present an approach for learning activity sequencing based on ontology and activity graph in personalized education system. Modeling and management of learning activity and learner are depicted, and an algorithm is proposed to realize learning activity sequencing and learner ontology dynamically updating.

Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models

Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery,which has an important impact on gas field development planning and economic evaluation.Owing to the model’s simplicity,the decline curve analysis method has been widely used to predict production performance.The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs.In this paper,a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed.The sequence learning methods used in production performance analysis herein include the recurrent neural network(RNN),long short-term memory(LSTM)neural network,and gated recurrent unit(GRU)neural network,and their performance in the tight gas reservoir production prediction is investigated and compared.To further improve the performance of the sequence learning method,the hyperparameters in the sequence learning methods are optimized through a particle swarm optimization algorithm,which can greatly simplify the optimization process of the neural network model in an automated manner.Results show that the optimized GRU and RNN models have more compact neural network structures than the LSTM model and that the GRU is more efficiently trained.The predictive performance of LSTM and GRU is similar,and both are better than the RNN and the decline curve analysis model and thus can be used to predict tight gas production.

An Incremental Time-delay Neural Network for Dynamical Recurrent Associative Memory

An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.

Learnability of Grammatical Sequencing: A Processability Perspective of Textbook Evaluation in EFL Settings

This study conducts an acquisition-based evaluation of four primary-school English textbook series used in China. The evaluation aims to determine whether the sequencing of grammatical structures in the series is compatible with the L2 learning sequence stipulated in Processability Theory (PT). The results show a partial agreement between the sequencing of structures as teaching objectives in the series and the PT-based processability hierarchy. The sequencing of structures in the initial stages is consistent with the learning sequence of L2 English stated in PT. However, several structures in the intermediate or high stages are taught in a deviant way against their sequencing in PT. The deviant grading of those structures is possibly associated with the theme-based guidelines adopted in the textbooks. It appears that concerns with the utility of grammatical structures in a given context take precedence over concerns for L2 development. A number of suggestions are offered to textbook writers in terms of the role of input, the learners, developmental readiness, and the issue of heterogeneity in L2 classrooms.