The Effects of Non-Directional Online Behavior on Students’ Learning Performance: A User Profile Based Analysis Method

Network behavior analysis is an effective method to outline user requirements, and can extract user characteristics by constructing machine learning models. To protect the privacy of data, the shared information in the model is limited to non-directional network behavior information, such as online duration, traffic, etc., which also hides users’ unconscious needs and habits. However, the value density of this type of information is low, and it is still unclear how much student performance is affected by online behavior; in addition there is a lack of methods for analyzing the correlation between non-directed online behavior and academic performance. In this article, we propose a model for analyzing the correlation between non-directed surfing behavior and academic performance based on user portraits. Different from the existing research, we mainly focus on the public student behavior information in the campus network system and conduct in-depth research on it. The experimental results show that online time and online traffic are negatively correlated with academic performance, respectively, and student’s academic performance can be predicted through the study of non-directional online behavior.

Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification

There are several techniques to select a set of traffic features for traffic classification. However, most studies ignore the domain knowledge where traffic analysis or classification is performed and do not consider the always moving information carried in the networks. This paper describes a selection process of online network-traffic discriminators. We obtained 24 traffic features that can be processed on the fly and propose them as a base attribute set for future domain-aware online analysis, processing, or classification. For the selection of a set of traffic discriminators, and to avoid the inconveniences mentioned, we carried out three steps. The first step is a context knowledge-based manual selection of traffic features that meet the condition of being obtained on the fly from the flow. The second step is focused on the quality analysis of previously selected attributes to ensure the relevance of each one when performing a traffic classification. In the third step, the implementation of several incremental learning algorithms verified the usefulness of such attributes in online traffic classification processes.

The Effect of Ownership Marketing Expertise on MLB Attendance and Digital Consumption

Scholarship has established that characteristics of a firm’s upper echelon affect firm-level outcomes in a range of industries. In professional sport, firms depend on live game attendance and, increasingly, the consumption of online content to generate local revenue. The ability to drive these two revenue streams depends on a franchise’s competencies in marketing, relationship management, and brand building. In this research, we speculate those competencies start at the top, i.e., with ownership. Using upper echelons theory (UET), we hypothesize that franchises with owners who have substantial marketing expertise are better able to drive attendance and online search traffic. Using a panel dataset of 30 teams over a 10-season period, we found that ownership expertise in marketing was generative of significantly more attendance but perhaps not significantly greater online traffic. The results are discussed in the context of UET, and implications for practitioners are presented.

The Effect of Ownership Marketing Expertise on MLB Attendance and Digital Consumption

Scholarship has established that characteristics of a firm’s upper echelon affect firm-level outcomes in a range of industries. In professional sport, firms depend on live game attendance and, increasingly, the consumption of online content to generate local revenue. The ability to drive these two revenue streams depends on a franchise’s competencies in marketing, relationship management, and brand building. In this research, we speculate those competencies start at the top, i.e., with ownership. Using upper echelons theory (UET), we hypothesize that franchises with owners who have substantial marketing expertise are better able to drive attendance and online search traffic. Using a panel dataset of 30 teams over a 10-season period, we found that ownership expertise in marketing was generative of significantly more attendance but perhaps not significantly greater online traffic. The results are discussed in the context of UET, and implications for practitioners are presented.