Using the Occupational Information Network (O*NET) to demonstrate the importance of understanding statistics for undergraduate students

Abstract

Summary Because of the increasing use of business analytics to improve business processes, it is not difficult to argue the importance that undergraduate business students gain the ability to understand, interpret, and communicate statistics. However, a task that may be difficult for statistics instructors is to quickly demonstrate and convince business students the importance of statistics. This paper presents an assignment that utilizes the Occupational Information Network (O*NET) to demonstrate the importance of understanding basic statistical concepts such as population parameters, sampling methods, descriptive measures, and displaying data. This can be administered to an introductory business statistics course in order to drum up students' initial interest and views of applicability of statistics.

The Construction and Application of E-Learning Curricula Evaluation Metrics for Competency-Based Teacher Professional Development

“…Because the highest numerical value of C B represented 31.98% of the nodes, it should be included in the elearning curricula evaluation metrics for CB-TPD. To sum up, using the SNA approach to obtain centrality metrics from the ECPC KCN to construct an evaluation framework for e-learning curricula evaluation metrics for CB-TPD and to verify its importance in the evaluation of e-learning curricula in the current study was in line with the research methods in previous studies [12,18].…”

“…From the results shown in Figures 10 and 11, which displayed the visualization of the centrality metrics of C WD and C D in the ECPC KCN, for all eight categories of educational professionals (according to the occupational titles in O*NET), at least 75.23% and 74.78% of ECPC keyword co-occurrence existed in more than three of the categories. The ranking and importance of the numerical values of C WD and C D [14], as represented by the characteristics of weighted co-occurrence and co-occurrence [18,25,26], respectively, were ≥3; therefore, they should be included in the e-learning curricula evaluation metrics for CB-TPD.…”

“…This research used social network analysis (SNA) methods, approaches, and theoretical concepts, such as affiliation networks and bipartite graphs [16] that included occupational titles, Element Name and ID, and KSAOs [17] from the O*NET database, to design and implement CB-TPD curricula, as shown in Figure 1. In addition, ECPC keyword co-occurrences were analyzed by centrality metrics to construct e-learning curricula evaluation metrics for CB-TPD and verify their importance [18]. The bipartite network in Figure 1 shows vertices K 1 to K 3 , S 1 to S 3 , A 1 to A 3 , and O 1 to O 3 , which represent the attributes of the KSAOs, and vertices T 1 to T 4 , which are the occupational titles of the teachers, and the lines between them connect the attributes of the KSAOs to the corresponding occupational titles.…”