Higher education blended learning: a network study of student learning paths

For the aim of this research, a model of students' learning pathways as a network that records time and sequence of learning events is introduced. These learning pathways networks also establish the online learning rate. Results demonstrate that pupils who passed learned more online than those who failed. The findings of the person-centered study demonstrate that only test anxiety affects the number of nodes and arcs of the individual learning pathway network, which was represented as an individual network. Also, the qualities of social networks are influenced by how active or inactive users are. There is evidence to back up the theory that pupils who are driven to learn do not necessarily study more information. As a result of exam anxiety, engagement, and disengagement, the learning pathways of individuals are shaped and reshaped throughout time.

Social-Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

Caching in device-to-device (D2D) networks is emerging a promising trend, which enables to reduce backhaul traffic. Moreover, social interaction among users influences the performance of overall system network. Therefore, it is crucial to consider social attributes in the D2D networks to develop a caching strategy to resolve the problem of unbalanced content distributed. In this paper, we consider two types of users according to their activeness, i.e., active users and inactive users. Inactive users assist active users cache contents during off-peak periods and provide the contents to the active users during peak periods to relieve the pressure of base station (BS). In addition, caching system model is divided into physical domain model and social domain model. In physical domain, the quality of communication links is judged by the delay between D2D users. In social domain, based on a real-world dataset, CiaoDVD, we calculate user similarity in three dimensions and obtain user trust by a trust topology to measure user relationships. Finally, in order to maximize the cache hit ratio, a joint action deep Q -networks (JADQN) framework is proposed to pair the active users with inactive users and distribute the contents to inactive users. Simulation results indicate that the proposed strategy improves the cache hit ratio by 42.9% and reduces the download delay by 48.8% compared with least frequency used (LFU) algorithm, which validates the effectiveness of our method.

The effect of the TF-IDF algorithm in times series in forecasting word on social media

Forecasting is one of the main topics in data mining or machine learning in which forecasting, a group of data used, has a label class or target. Thus, many algorithms for solving forecasting problems are categorized as supervised learning with the aim of conducting training. In this case, the things that were supervised were the label or target data playing a role as a 'supervisor' who supervise the training process in achieving a certain level of accuracy or precision. Time series is a method that is generally used to forecast based on time and can forecast words in social media. In this study had conducted the word forecasting on twitter with 1734 tweets which were interpreted as weighted documents using the TF-IDF algorithm with a frequency that often comes out in tweets so the TF-IDF value is getting smaller and vice versa. After getting the word weight value of the tweets, a time series forecast was performed with the test data of 1734 tweets that the results referred to 1203 categories of Slack words and 531 verb tweets as training data resulting in good accuracy. The division of word forecasting was classified into two groups i.e. inactive users and active users. The results obtained were processed with a MAPE calculation process of 50% for inactive users and 0.1980198% for active users.

Learning in Online Communities: Behavioral Strategies of the Users of Educational Social Networks

The article deals with the results of a study on users’ behavioral strategies in educational social networks as Internet platforms for the communities of students and teachers who collaborate to solve educational problems. According to the analysis of the students and teachers’ activity on the educational social network of the Volgograd State Socio-Pedagogical University, the author has determined the groups of users with similar behavioral strategies, namely: community organizers, activists for communication and document exchange, educational assignment performers, information consumers, and inactive users. The author provides generalized portraits of each group, their numerical composition, as well as comparative characteristics in relation to other groups. The manuscript concludes that the presented groups reveal typical behavioral strategies of the users of educational social networks, which further development of educational platform tools, as well as pedagogical technologies based on their implementation should consider.

An Improved Slope One Algorithm Based on User Similarity Weights

This paper presents a Slope one improved algorithm based on user similarity and user interest forgetting function. Aiming at the problem of large number of users and a lot of noise data, the inactive users are filtered out by setting the threshold of user activity, and then the neighbors of the target users are obtained through the calculation of user similarity. According to interest forgetting function, and then filter out items that have less effect on current users to reduce the noise data to improve the accuracy of the algorithm. Experimental comparison shows that the improved algorithm has better accuracy than the commonly used weighted Slope one and two-pole Slope one.