Examining Undergraduate Students’ Attitudes toward Business Statistics in the United States and China

The rapid growth of analytics is bringing more attention to quantitative core curriculum requirements in undergraduate business programs. Statistical knowledge and skills are unequivocally recognized as essential cornerstone of business analytics. Furthermore, educational research has shown that academic performance in statistics classes is related to the attitudes that students bring to the course. This article assesses the reliability and validity of the Survey of Attitudes toward Statistics (SATS) in measuring noncognitive dimensions of attitudes among undergraduate business students. Sample data from U.S. and Chinese introductory business statistics classes were collected and analyzed to learn more about this aspect of student engagement across business schools located in countries with substantially different levels of success in international mathematics achievement testing, as well as differing cultural and educational practices. Results show that the six‐factor model structure of the SATS provides a good fit in both populations, with students entering business statistics holding only slightly positive attitudes toward the subject. Significant distinctions between four of the six attitude components were identified. Implications of measuring and improving these attitudes are discussed. Business statistics instructors are encouraged to use the survey as a standardized instrument to measure effects of interventions and make evidence‐based pedagogical decisions.     

Beyond engagement analytics: which online mixed-data factors predict student learning outcomes?

This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large sample from an online undergraduate business course at an accredited American university (n = 228). Recent studies have discounted the ability to predict student learning outcomes from big data analytics but a few significant indicators have been found by some researchers. Current studies tend to use quantitative factors in learning analytics to forecast outcomes. This study extends that work by testing the common quantitative predictors of learning outcome, but qualitative data is also examined to triangulate the evidence. Pre and post testing of information technology understanding is done at the beginning of the course. First quantitative data is collected, and depending on the hypothesis test results, qualitative data is collected and analyzed with text analytics to uncover patterns. Moodle engagement analytics indicators are tested as predictors in the model. Data is also taken from the Moodle system logs. Qualitative data is collected from student reflection essays. The result was a significant General Linear Model with four online interaction predictors that captured 77.5 % of grade variance in an undergraduate business course.     

Bridging the gap between supply chain management practice and curriculum: A location analytics exercise

This teaching brief explores how and why location analytics should be taught in business schools using three objectives. First, an explanation is provided for the importance of including location analytics in the standard business school curriculum—especially in the field of supply chain management. Second, a lack of GIS-based location analytics methods in business school curricula is demonstrated. Third, a three-part location analytics exercise is introduced to contribute to the supply chain management curriculum. The proposed exercise utilizes the output of a GIS-based location analytics software (namely, Esri's ArcGIS Online) as the input for a location set covering problem that can be solved using an integer programming solver. The exercise can also be used as a stand-alone example of GIS in a supply chain management course. This teaching brief aims to (1) develop a new method to use in teaching location analytics in supply chain management and analytics courses and (2) bridge an important gap between supply chain management practice and curriculum.     

Teaching introductory business statistics

This paper seeks to compare the traditional delivery of introductory courses in business statistics within a business and management faculty, with that of an approach that seeks to teach through information technology (IT). To achieve this objective this paper will examine how traditionally taught courses in business statistics are delivered and compare this with a teaching approach through the use of IT     

Knowledge,Skills, and Abilities for Entry‐Level Business Analytics Positions: A Multi‐Method Study

It is impossible to deny the significant impact from the emergence of big data and business analytics” on the fields of Information Technology, Quantitative Methods, and the Decision Sciences. Both industry and academia seek to hire talent in these areas with the hope of developing organizational competencies. This article describes a multi‐method research agenda that was executed to ascertain insights regarding which knowledge, skills, and abilities, (KSAs) are valued by employers seeking to hire entry‐level analytics professionals from schools of business. Current undergraduate business analytics programs are first examined to define the research scope. A triangulated mixed‐method research approach is then used to determine the knowledge, skills, and abilities that are in demand for entry‐level jobs in this area. Finally, the multi‐method triangulation of data is combined with experiences in building academic programs in business analytics at two nationally‐ranked state universities to offer insights for those seeking to develop academic programs in this area.     

Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course

In this article, we present an experiential perspective on how a big data analytics course was designed and delivered to students at a major Midwestern university. In reference to the MSIS 2006 Model Curriculum, we designed this course as a level 2 course, with prerequisites in databases, computer programming, statistics, and data mining. Students in the class were mostly seniors or at the graduate level, and had a strong technical and quantitative background. We include details of concepts covered in the course, as well as summaries of four major sample course assignments used. Some of the concepts covered include large‐scale data collection and management using the Hadoop ecosystem, stream mining, visual analytics, and social network analytics. Besides Hadoop, the course also introduced various IBM and Teradata big data tools. We show how the course modules align with the intended learning goals and course objectives. A post‐course survey indicated that the structure and organization of the course helped students clearly and concisely assimilate the course content.     

Helping Business Students Acquire the Skills Needed for a Career in Analytics: A Comprehensive Industry Assessment of Entry‐Level Requirements

Fact‐based decision making is changing job functions within organizations more than any other technology. Analytics, once the purview of the data scientist, is now spread throughout organizations. No longer is there a single job title, job function, or set of required skills and credentials for an analytics career. Companies have moved away from seeking applicants with a specific degree to now recruiting analytics talent based on required skill sets. For more than a decade, business schools have been developing new programs in analytics in response to industry's needs. However, in developing meaningful career‐ready professionals, business programs must understand the skills required across different analytics job functions. In this article, the authors present a comprehensive assessment of the skills sought by employers when considering a candidate for an entry‐level analytics position. The authors describe the demand for various types of analytics professionals, identify the job titles and functions with the most significant demand, and then draw a comparison of the job requirements of hard skills, soft skills, software skills, and credentials between three of the most sought‐after analytics areas: data science, data analytics, and business analytics. The authors conclude by providing faculty and administrators with recommendations on how to adapt their courses and programs to provide students with the fundamental preparation necessary for careers in data science, data analytics, and business analytics.     

Determining Optimal Skillsets for Business Managers Based on Local and Global Job Markets: A Text Analytics Approach

We are experiencing a significant shift in management practices—moving from intuition, experience, and gut‐feeling driven decision‐making to one that is driven by data, evidence, and computational sciences. This shift, which is often called the analytics revolution, is not only changing the business landscape from a practical perspective but also redefining the white‐collar job market. The goal of this study is to employ a data‐ and analytics‐driven approach to analyze a large and feature‐rich dataset (which is composed of the recent job postings and the extant published literature) to characterize the state of the current business job market, especially to magnify the analytics related features and expectations including the geographic (i.e., local vs. global) differentiators. Using several graphical and tabular representations, in this paper we report on our thought‐provoking findings that collectively illustrate the changing face of knowledge, skills, and abilities required by the current local and global business job markets.     

Exploratory Analysis to Identify Concepts,Skills, Knowledge,and Tools to Educate Business Analytics Practitioners

Recent technological advancements in data storage and processing have changed how companies conduct their business. An increasing number of firms have started putting their efforts in extracting information from their databases to improve profitability and reduce costs using quantitative approaches. Thus, the job market has been experiencing a rapidly growing demand for business analytics (BA) practitioners, and universities across the globe are increasingly responding to this newly emerged field by offering both undergraduate and graduate level degrees as well as certificate programs. Thus, this research aims to provide a framework for academic institutions to develop a state‐of‐the‐art master's in business analytics (MSBA) curriculum by identifying concepts, skills, knowledge, and tools (CSKT) that industry seeks in BA practitioners. Our data‐driven methodology utilizes peer institution analysis, indeed.com web scraping, and focus group analysis with mid‐ and senior‐level analytics leaders from major companies. Our contribution to the literature and recommendations to institutions developing MSBA programs are offered at the end.     

Experiential learning of management principles via online student-manager discussions

This teaching brief describes an experiential project used in a graduate Principles of Management course for nonbusiness undergraduate students. Groups of four to six first-year MBA students interviewed a seasoned manager online twice over the 8-week course and discussed the applications of course material. Project subtopics included an introduction to management, strategic management, ethics and social responsibility, innovation and change management, international business, organizational structure, authority and job design, human resource management, leadership, and communication, operations management, and business analytics. Students completed a group report and an individual reflection on their experience. Over 92% of graduate students in the class indicated that the project was a positive learning activity.     

A Project‐Based Approach to Executive Education

Executive education (EE) has been an important part of business school offerings for nearly as long as there have been business schools. Similarly, business schools were among the first in higher education to adopt online approaches as a means for course delivery. Despite this experience, few business schools have been able to successfully integrate EE with online delivery approaches. This study suggests that a project‐based approach can achieve EE/online delivery integration. The case is first made for a project‐based approach by telling the story of our institution's journey toward a project‐based EE model. Challenges, successes, and plans for the future are then discussed.     

In Their Own Words: A Text Analytics Investigation of College Course Attrition

Excessive course attrition is costly to both the student and the institution. While most institutions have systems to quantify and report the numbers, far less attention is typically paid to each student’s reason(s) for withdrawal. In this case study, text analytics was used to analyze a large set of open-ended written comments in which students explained their reason(s) for course withdrawal in their own words. The text for all comments was extracted verbatim from the course withdrawals database of Florida State College at Jacksonville, a large, diverse, multicampus institution located in northeast Florida. An initial set of 616 comment records from the beginning of the fall 2010 term was used to develop a preliminary text analytics model. This model revealed 11 major category nodes and successfully classified 96.1% of all withdrawal records into one or more categories. The model was retained and further tested using a second set of 679 records from the spring 2011 term and found to successfully classify 98.7% of the spring records. At the broadest level, withdrawal explanations were found to include both academic and nonacademic student rationales. Leading academic rationales involve course scheduling adjustments, delivery method preference changes (e.g., classroom vs. online), and faculty related reasons. Leading nonacademic rationales include personal issues especially involving job/work, family, financial, and health matters. The limitations of the study along with implications for practice, administrative decision making, and future directions for the expanded use of text analytics in institutional research are discussed.     

学习分析:大学教学信息化研究与实践的新领域

随着大学教学信息化进程的不断深化与发展,如何测量、挖掘、分析与利用在学习平台中积累的大量数据,以更好地支持教学与学习,进而改善和提升教与学的质量与效能,已成为摆在大学教学信息化研究者与实践者面前的现实课题。学习分析的兴起则为解决这一难题,提供了一系列的方法、工具和手段。文章将基于国内外学者的已有研究,通过对学习分析的发展脉络的梳理,对学习分析的内涵、过程、工具和方法作较为深入的解读,并尝试从推动大学教学创新的视角,阐释学习分析对促进大学教学信息化深入发展的重要价值和深远影响。     

Online homework impact in undergraduate mathematics and business statistics courses

This evaluation examines the impact on student success rates related to changes in instructional programmes in undergraduate mathematics and statistics courses. Success for students taking courses with a computer-based homework component was compared with success of students who took the course in prior semesters without the computer-based component. Graphical and analytical tools are used to compare results. Results come from multiple semesters of each type of homework application, for both pre-calculus algebra and business statistics courses. Students whose performance is utilised in this study are undergraduate students taking introductory level college mathematics or business statistics courses, with mostly no prior instruction at this level. Comparing the success of the intervention group with the success of the baseline control group, findings support that the students using the computer-based homework instruction are just as successful as those using the traditional method of homework instruction. Utilising the online homework applications, provide several important advantages in today’s universities, including the reduced time for faculty grading, consistency of graded assignments across all sections of a course and most importantly, immediate feedback for students.     

Application of learning analytics using clustering data Mining for Students’ disposition analysis

Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research work is to find meaningful indicators or metrics in a learning context and to study the inter-relationships between these metrics using the concepts of Learning Analytics and Educational Data Mining, thereby, analyzing the effects of different features on student’s performance using Disposition analysis. In this project, K-means clustering data mining technique is used to obtain clusters which are further mapped to find the important features of a learning context. Relationships between these features are identified to assess the student’s performance.     

人际沟通分析学在心理健康课程中的应用与效果检验

本文采用质性分析方法,对66份首都经贸大学人际沟通分析学作业进行分析,检验人际沟通分析学在大学生心理健康教育中的教学效果,结果发现：选修人际沟通分析学的学生在心理幸福感的六项机能上有不同程度的改善,意味着幸福感的提升。这一结果肯定了人际沟通分析学在心理健康教育中的作用。     

Towards Inclusive Teacher Education: Sensitising individuals to how they learn

Higher education has struggled to acknowledge and translate into better teaching and learning practices that sizeable literature base suggesting a link between cognitive style, learning preferences, and performance. Research is reported in which 80 undergraduate students on a primary education degree were studied to examine the relationship between their cognitive style, their learning preferences, and perceived impact on their teaching practices. All students completed the CSA measure of cognitive style, the ASSIST, two further questionnaires exploring learning preferences and perception of good teaching during the course, and an evaluation at the end of the teaching unit. Significant differences were found between the three cognitive styles investigated: wholist, intermediate, and analytic. In terms of learning preferences, using ANOVA statistically significant differences were found between the three styles with wholists being most concerned about speed of delivery and least liking computer‐assisted learning. In addition, wholists preferred less structure than analytics in their teaching and claimed to use more images while analytics claimed to use more speech in their teaching. Intermediates demonstrated a greater preference for tangential approaches to teaching and were least happy with the nature of the teaching they had received while at university. Many of the differences reported in the literature between the different cognitive styles were not evident in this study. However, the interpersonal and intrapersonal characteristics of wholists and analytics, respectively, were evident and perceived to impact on planning and delivery in the classroom. While further school‐based research involving greater numbers is required, interest in learning styles remains especially relevant if one intends to offer a truly inclusive education for all learners.     

Designing SQL Database Modules Using Correlation Coefficients and Linear Regression: A Content‐Centered Approach

Information systems educators are increasingly incorporating business intelligence and analytics topics into information systems training. Educators often create modules independently, which can be difficult to effectively design given most information systems trainers have no formal training in instructional design. This article incorporates a content‐centered design model, pebble‐in‐the‐pond (Merrill, 2002 ), and provides an example of the design process using the topic of predictive analytics that information systems educators can follow to create instructional modules using sound instructional design considerations.