Treffer: Transitioning a Traditional Introductory Information Systems Course to a Data Analytics Focused Course
Postsecondary Education
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Firms desire graduates capable of executing current and future business practices, many of which revolve around data. To meet those needs, we shifted the orientation of our required information systems course from technology to data. Instead of a survey of information systems, students learn the data acquisition-preparation-mining-presentation process in an information-systems setting. The scope of the revised undergraduate introductory course includes decision trees, Bayesian classifiers, and clustering; it uses Microsoft's Excel, Access, SQL Server, Power BI, and SQL Server Analysis Services to reveal the basics of data analytics to students. Students have welcomed the change and understand that the course material directly applies to what they will experience as working professionals. Similarly, employers have appreciated the change and tell us that our graduates are better prepared to perform data analysis with minimal training.
As Provided
AN0159815179;q1n01oct.22;2022Oct26.04:12;v2.2.500
Transitioning a traditional introductory information systems course to a data analytics focused course
Firms desire graduates capable of executing current and future business practices, many of which revolve around data. To meet those needs, we shifted the orientation of our required information systems course from technology to data. Instead of a survey of information systems, students learn the data acquisition–preparation–mining–presentation process in an information‐systems setting. The scope of the revised undergraduate introductory course includes decision trees, Bayesian classifiers, and clustering; it uses Microsoft's Excel, Access, SQL Server, Power BI, and SQL Server Analysis Services to reveal the basics of data analytics to students. Students have welcomed the change and understand that the course material directly applies to what they will experience as working professionals. Similarly, employers have appreciated the change and tell us that our graduates are better prepared to perform data analysis with minimal training.
Keywords: analytics; course design; undergraduate education
INTRODUCTION
Information systems (IS) education comprises a single class for many business majors. While some students see value in learning about business technology, many students not majoring in IS feel the class is irrelevant (Aytes, 2004). Unfortunately, they believe that IS departments in their workplaces will meet their technology needs, and that their roles in operations, marketing, finance, accounting, or management are isolated from information systems. Overcoming this misconception has been a longstanding challenge of IS education (Hershey, 2003; Pridmore et al., 2010).
To reduce our students' IS apathy, we asked recruiters about the skills their organizations seek. Our external stakeholders include employers spanning service, manufacturing, distribution, retailers, utilities, state and local government units, and university alumni. Employers have strong opinions about what is important for IS education, and though responses varied, some recommendations were nearly universal. Recruiters want students to be data literate and skilled in analytics. This data‐driven approach reflects a broader trend in both academia and industry.
RELATED LITERATURE
We determined that the best way to meet this data‐driven approach was by examining applicable learned proficiencies in analytics for our students, beginning with identifying the skills graduates need to flourish in the world of commerce. Due to its importance, the literature in this area is expansive, so we broke it down into several key steps to best inform our course redesign.
Entry‐level Job Requirements
The first step in the redesign process was identifying an optimal skillset for business students upon graduation; this is an ongoing challenge as businesses continue to evolve. Cegielski and Jones‐Farmer (2016) used a qualitative Delphi study, a survey, and an archival content analysis to find the knowledge, skills, and abilities (KSAs) sought by employers seeking to hire entry‐level business school graduates. They found that a business analytics curriculum should use an experiential learning framework that includes analytics problem solving, integrative and predictive analysis, and decision making to equip graduates for professional success. Desired technical KSAs included Excel, SQL, SAS, and R.
Nasir et al. (2020) used text mining applied to job listings to pinpoint the top employer‐specified KSAs in analytics (data visualization, data mining, and regression), business (team building, business acumen, customer service, project management), and technical domains (Hadoop, machine learning, Python, SAS). Lang et al. (2015) discovered that organizations see soft skills as more consequential than hard skills for starting IS positions, information that extends to first‐job business positions. Prominent soft skills cited were critical thinking and continual learning, while employers listed Microsoft Office, security, and database skills as most needed hard skills. Leonard et al. (2019) found that IS curricula and employers' desired KSAs differ in several aspects, but that the two foremost technical skills were Microsoft Office and Database‐Data‐Warehouse‐SQL. Rienzo and Chen (2018) found that employers hiring analytics talent seek database, spreadsheet, project management, and statistics‐related analytics skills. In addition, most corporate recruiters prefer hiring recent graduates who have industry‐specific skills rather than those who need training (Pothier & Condon, 2019).
Analytics incorporation into the curriculum
As early as 2011, Chiang et al. (2012) recognized the opportunity for traditional IS programs to incorporate business intelligence and analytics topics into the IS curriculum. Wilder and Ozgur (2015) prescribe a seven‐course undergraduate analytics degree with a framework that includes Data Management via SQL, Descriptive Analysis, Data Visualization, Predictive Analytics, Prescriptive Analytics, and Data Mining using the Cross‐Industry Standard Process for Data Mining (CRISP‐DM); Burns and Sherman (2022) describe a successful Business Analytics Minor curriculum informed by the research of Wilder and Ozgur. Lawler and Molluzzo (2015) specify a big‐data analytics concentration, while Jacobi et al. (2014) created an interdisciplinary IS curriculum design model supported by a sophisticated curriculum generator. Clayton and Clopton (2019) describe the integration of analytics into the business curriculum of a private university with accreditation from the Association to Advance Collegiate Schools of Business International (AACSB), beginning with changes to their auditing course. Paul and MacDonald (2020) provide comprehensive undergraduate and graduate curriculum recommendations with templates to design new analytics programs or to appraise and alter existing programs. Urbaczewski and Keeling (2019) suggest that Management Information Systems (MIS) departments may eventually transition into analytics departments, given the mutual aim of providing support and improving the quality of decision‐making in organizations; this would entail dramatic curriculum alterations.
Incorporating analytics into courses
The third step in the redesign process was determining the best methods for incorporating data analytics into our IS course. A meta‐analysis definitively established the effectiveness of experiential learning by Burch et al. (2019). Self‐directed, experiential learning, including problem‐based learning (PBL) and project‐based learning (PjBL) techniques, improves learner success (Yazici, 2020). One example is how Bayley et al. (2021) used gamified project‐based learning (PBL) to amplify student engagement in a business analytics course. When Yazici (2020) extended PBL and its solution orientation by using PjBL to add projects with deliverables to teach statistics, programming, data management, and domain knowledge in an introductory course, students' had improved learning outcomes. In an introductory IS course, Chen et al. (2022) introduced artificial intelligence technologies to undergraduate business students. They used a gamified social entrepreneurship startup project to enhance their critical‐thinking skills and prepare them for the world of commerce. Ariyachandra (2020) provided a case study using a data‐visualization project where analytics were incorporated into an introductory IS course. Students responded positively, highlighting the increased experiential learning levels. Ceccucci et al. (2020) identified a database course and a predictive analytics course as the top two overlapping courses in schools with overlapping programs in business analytics and information systems Other efforts involve incorporating a programming component into existing courses or developing new courses (Asamoah et al., 2017; Brau et al., 2020; Frydenberg & Xu, 2019; Holman, 2018; Podeschi & DeBo, 2019). Experiential learning allays two concerns regarding introductory information systems courses: (1) that they provide insufficient value to non‐IS students due to an IS major recruitment orientation, and (2) that they emphasize technical skills while neglecting the impact of technology in decision‐making processes (Modaresnezhad & Schell, 2019).
Analytics across the disciplines
The fourth step in the redesign process was to determine how to best prepare students with the data analytics skills they would need throughout their college education, regardless of their business major. The worlds of both the practitioner (Wright, 2016) and the academic (Wymbs, 2016) recognize the multidisciplinary nature of analytics (Chen et al., 2012). The need for broad analytics literacy reaches beyond any individual job title, skillset, or credentials (Stanton & Stanton, 2020). All business professionals will consume analytics, and through self‐service tools, many will produce analytics (Bani‐Hani et al., 2018). As such, college graduates were traditionally expected to have computer‐literacy skills. These expectations have eventually shifted toward data‐literacy skills (Johnson et al., 2006). Data‐driven decision‐making has become the standard for business professionals (Alpar & Schulz, 2016).
AACSB alignment
The fifth step in the redesign process was to ensure that our plans would better prepare students for their chosen business professions while ensuring our school retained its AACSB accreditation. The MaCuDe project (Management Curriculum for the Digital Era, https://macude.org/), in cooperation with the AACSB, finds that data‐management skills (i.e., understanding and structuring data) are among the foundational skills expected of all knowledge professionals (Lyytinen et al., 2020; Topi, 2019). Standard 4 of the AACSB, 2020, Guiding Principles and Standards (as well as the AACSB, 2020 Interpretive Guidance) requires that curricula along with assurance of learning (AoL) processes and outcomes be current and relevant (AACSB, 2020, 2021). These changes point to the ever‐increasing need for the business curriculum to incorporate significant data analytics elements.
IS 2020 alignment
The sixth and more recent step in the redesign process was to incorporate the IS Realignment, a central component of the IS2020 report
Influence on course design
Overall, these research steps informed the evolution of our IS course from a traditional format to a data analytics format. Specifically, from the standpoint of the IS2020 model curriculum, our course replaces Foundations of Information Systems with a Data and Information Management course, including aspects of its elective competencies (Business Analytics, including Data Mining, Business Intelligence, and Data Visualization; Leidig & Salmela, 2021). In 2016, we shifted the course orientation from information systems to data analytics (data acquisition, preparation, mining, and visualization), aligning with the data analytics undergraduate business curriculum reported by Wymbs (2016) and the IS2020 updates (Leidig & Salmela, 2021). The model we adopted assumes that core analytics courses appropriately focus on database management, data mining, data warehousing, and programming skills. Our redesigned course incorporates the first three elements, providing students with the foundational knowledge, skills, and dispositions to flourish in the world of analytics.
STUDENT BACKGROUND
All business students, regardless of major, must take the Information Systems for Data Analytics (IS) course at our university; they often do so during their junior year. The course has only two prerequisites (Principles of Accounting I and Business Statistics I), though most students have also taken Business Statistics II and Principles of Accounting II due to our program of study. The prerequisites provide a foundation on which to build applied information systems and data analytics principles, and our course materials use concepts from both prerequisites to connect their business studies. For example, we use pivot tables' central tendency and dispersion measures to demonstrate how to summarize a data set quickly. The database we use reflects accounting principles applied to data organization. An example is querying the purchasing and production schemas to identify the products comprising the accounts payable for a vendor. Consistent with what Wang et al. (2018) found, our conversations with students suggest that our IS course is similar in difficulty and effort to business statistics; students share just marginally positive attitudes toward both courses.
COURSE ORGANIZATION
Learning goals and objectives
The course we describe is the only IS course offered in our university's undergraduate business curriculum. The original course was a traditional IS survey, covering information technology infrastructure, data‐resource management, applications, and managing information systems. Given employer feedback, we concluded that this curriculum did not equip our students with the skills needed by the market. One of our employer advisory council members commented, "graduates that know lots of details about systems are not what we need. What we need are problem solvers that can use data to answer questions." Another mentioned, "when we hire someone, we expect them to be ready to use data to support their recommendations." A third council member said, "if new employees are not ready to use data, their value is much less than new hires that are ready."
Consequently, we established a goal of providing an analytics overview focusing on data‐management skills. Pursuing data skills led us to adopt four specific course goals. First, students should understand the characteristics of the data economy, including its changing nature and the increasing role of IS within that economy. Second, students must recognize the ethical issues arising from the data‐related changes in work. Third, students need to understand the insights from the data provided. We included stories of professional success by our graduates and projects that demonstrated the power of data analysis; we also encouraged professional visits because students often accept advice from professionals more readily than from professors. Fourth, we wanted students to know data storage and retrieval principles, a solid grasp of which allows students to operate whatever future software package they need.
To meet these overarching goals, guide course development, and maintain consistency with university and AACSB requirements (Attaway et al., 2011), we established the following course objectives:
The data economy is a model that both replaces capital with data (Farboodi & Veldkamp, 2021) and acknowledges the topology of work in the information age (Géczy, 2015). It comprises a global digital ecosystem that enables organizations to incorporate vendors, partners, patrons, and customers into their information systems. We use the ubiquity of information and communication technology to motivate students to learn how to survive and thrive in the data economy. Ethical decision‐making within the data economy comprises an awareness of privacy considerations and an understanding of the potential for abuse (Taylor, 2017). Students demonstrating data acquisition, preparation, mining, and presentation skills cover the full analytics lifecycle. These changes align with the key components of Standard 4.1 of the AACSB Standards identified in 2020 by improving the relevancy of offered content and the focus on student competencies (AACSB, 2020).
As industry and recruiting experts suggested, we meet the first of these standards by shifting our focus from the traditional IS survey to a data analytics course. We meet the second by focusing our assessment on students' competency with specific tasks (as shown in objective 3) instead of their knowledge of those tasks; students achieve competency primarily through course activities and projects instead of traditional testing. Finally, we meet the third standard by bringing the course design in line with the goals of our college, specifically:
Course structure
To reach our goals and ensure that students met our course objectives, we began our design following the order suggestion in
1 TABLESegments
We start the course with an overview of analytics, using the CRISP‐DM methodology as a blueprint to manage typical analytics‐related projects. CRISP‐DM comprises business understanding, data understanding, data preparation, model building, testing & execution, and deployment (Sharda et al., 2018). The CRISP‐DM exposition aligns closely with the CRISP‐DM prescription discussed by Jaggia et al. (2020) and Miao et al. (2018) in terms of experiential learning, data understanding, data preparation, storytelling, and problem solving.
After introducing analytics, we begin the first of our eight learning segments using Microsoft Excel™. We use an in‐class list‐to‐relationships transformation followed by a similar homework assignment. We added preparation and analysis components to tailor these exercises to our focus, providing the first examples of the repeated themes of data acquisition, preparation, mining, and visualization.
Next, we repeat the pattern with Access, then SQL. Once we have taught students the basics of SQL, we add data mining to the sequence. We guide students through converting the data into each successive form. Students gather data from the SQL Server, work with large data sets, and become familiar with common data sources. The early assignments in the course are small, project‐like exercises. For instance, when learning SQL querying, students are asked to find their best customers using an early marketing predictive modeling technique that is still widespread, the Recency–Frequency–Monetary value (RFM) framework (Blattberg et al., 2008). Before the assignment, we explain the framework as marketing metrics that quantify customers' transaction history, where recency is the time since the customer last made a purchase, frequency is the rate of purchases, and monetary value is the currency exchanged. RFM categorizes customers and calculates their likelihood of responding to offers.
The process of answer discovery begins with identifying which tables contain the relevant data; students then build queries to retrieve the data required to answer the question. Other assignments follow a similar pattern: we present a problem as a business question, and students identify what data tools can be used to find the answer. In addition to obtaining each answer, students must present their findings effectively (e.g., using visualizations, written reports, or PowerPoint presentations). Each assignment includes specific deliverable requirements, requiring a format appropriate for a business setting. These project‐oriented analytics tasks ensure that students are introduced to the communication, project management, and presentation skills needed to be successful professionals (Johnson et al., 2020).
We introduce each segment to students through an analytics lens based on effective data organization. As Ruiz (2017) explained, without well‐structured data, analysis suffers from tedious and time‐consuming searching. However, organizing data according to solid data‐modeling principles reduces data cleansing and preprocessing later (Snyder, 2019). We emphasize detecting modification anomalies (i.e., update, insert, and deletion) and their attenuation using the relational model's correct use. Normalization concepts explain how primary and foreign keys work to create relationships.
There are three benefits of an analytics focus. First, students understand why organizations store data in relational databases. Second, they learn to create a simple database or participate as a team member in creating a complex database. Third, students can analyze existing databases because they understand database development, structure, and maintenance tasks. We extend these last two benefits with entity‐relationship (ER) diagramming, which demonstrates that designing databases does not require an in‐depth mastery of programming.
Student skills
Our third objective is to help students learn how to acquire and prepare data, use data mining, and present results. To do this, we introduce students to the following programs: Microsoft Excel, Microsoft Access, Microsoft SQL Server, Microsoft Power BI, and Microsoft SQL Server Analysis Services. More importantly, we help them develop four specific skill areas: working with spreadsheets for data analysis, retrieving data, data analytics, and interpreting data‐mining models. Assignment examples with their associated tools are summarized in Table 2.
2 TABLESoftware used
We help students develop their skills in working with spreadsheets using Microsoft Excel. Initially, we illustrate that spreadsheets are primarily designed for data analysis with limited storage capabilities. We introduce the students to modification anomalies and how they can be avoided with functions like VLOOKUP. This allows us to explore anomalies, clarify why relational databases use tables for data storage, and explain their relationships. Note that while XLOOKUP is a more capable and general version of the LOOKUP family of functions than VLOOKUP, Microsoft released XLOOKUP in 2019, 3 years after we changed the course format. We are in the process of replacing VLOOKUP with XLOOKUP; for more information, see the link in the references (McDaid, 2019).
In addition to helping students learn specific programs, we also emphasize why a good database design requires that data be normalized. Normalization reduces or eliminates data redundancy and inconsistency by storing data in one location only; it also minimizes spelling and transposition errors and ultimately decreases or prevents deletion, insertion, and update anomalies. We describe deletion anomalies as deleting the only instance of data in the database, for example, an anomaly arising from deleting the last vendor tuple that records the vendor's product category. For insertion anomalies, that is, being blocked from adding data unless other data is inserted first, we illustrate the problem with examples such as the inability to add an employee with a new classification. Update anomalies arise from updating one instance of redundant data without updating other duplicate data. An example we use is a clinic's record of a family that moves, but only the address of the responsible party gets updated while the dependents' data still records the old address. These issues are useful for explaining primary and foreign key concepts, which initially seem abstract to most students. To contrast data analysis and storage concepts in spreadsheets, we introduce the students to goal seek and what‐if analysis techniques to illustrate Excel's capability. Once students are more familiar with the relational databases (described in the next paragraph), we use the PowerPivot add‐in to demonstrate how to connect Excel to external data sources and use those sources for multidimensional analysis. At that stage, we also revisit categorical and numeric data concepts in the form of dimensions and measures. Depending on the semester, we assign three to four Excel assignments: one for using VLOOKUP to resolve modification anomalies, one or two for goal seek and what‐if analysis, and one for using the Power Pivot add‐in. The VLOOKUP assignment introduces students to matching records in separate worksheets based on a common column. The main rationale behind this assignment is to provide an analog for how primary and foreign keys work in relational databases. The major goal behind assignments incorporating what‐if analysis and goal seek function is to gently introduce students to the variable concept, assign a value to it, and refer to it in a model. Students' use of cell references in the formulas for each assignment teaches them how to pass values between different calculations and maintain the model's internal consistency. The PowerPivot assignment is introduced in the latter part of the semester to illustrate how dimensional data warehouse tables can be incorporated into Excel's data model and how dimension and fact tables can slice and dice data. We also introduce students to the drill‐down concept using hierarchical relationships between dimensions.
Next, we help students develop their skills in data retrieval skills using SQL. We provide the students with an overview of the parts of SQL but focus primarily on the SELECT data manipulation language (DML) statement. In this part of the course, the students learn how to retrieve data from relational tables to answer basic reporting or analysis problems. This focus allows us to develop students' skills in retrieving data, sorting data, filtering data, creating calculated fields, using data‐manipulation functions, summarizing data, grouping data, and querying multiple tables. These topics are introduced through questions that could arise during normal business operations. We assign four homework activities to test the students' skills using the SQL SELECT statement to answer business questions. In the first assignment, students are provided directions on where to look for the needed data. As they move into the more advanced assignments, they are provided with only general hints and are left to translate the business question into technical SQL terms.
Third, we help students develop their skills in data analytics using Microsoft Power BI. The students learn how to import data from Excel files, SQL Server databases, and web pages. We also focus on data‐cleansing techniques such as splitting data into multiple columns, concatenating data‐type modification functions, and replacing existing values. Power BI also allows students to revisit data‐visualization techniques they learned in their introductory statistics course (e.g., bar and pie charts, histograms, pivot charts). It introduces techniques such as data drill‐down and geographical and treemap use, as well as advanced sorting techniques. We assign two Power BI homework assignments in the course: one for connecting to a web data source, data preparation, and building basic visualizations, and one for creating more advanced visualization on top of a well‐structured SQL Server database.
Fourth, we help students develop their skills in building and deploying data mining models for binary classification in predictive models using SQL Server Analysis Services and Microsoft Visual Studio. Here, the students revisit the concepts of independent and dependent variables using decision trees, Bayesian classifiers, and clustering techniques. We use Microsoft Visual Studio for students to build their business intelligence projects. They first learn to create project data sources (from an existing data warehouse in SQL Server) and then deploy data‐mining models for those data sets. We conclude with an assignment in which students build a predictive model to identify which customers are more likely to purchase a product based on historical sales and demographic data. In addition, they also produce a short report discussing their findings. See Appendix A for the details of software use.
COURSE DELIVERY
Designing class sessions
This course implements a flipped‐classroom approach to realize the benefits of interactive learning (Swart & Wuensch, 2016; Tiahrt & Porter, 2016). Thus, students must watch a 5‐ to 15‐min prerecorded video lecture before the beginning of each new topic. To further assure that students are familiar with the underlying concepts for each assignment, we administer nine quizzes throughout the semester. These open‐book quizzes are administered at the beginning of the first session covering a new topic; students are given two attempts at each quiz, with the highest score kept. After the quiz, we present a 15‐min mini‐lecture reviewing each topic. These lectures emphasize key concepts, answer questions, and highlight practical applications of the materials students have already learned before class. To the best of our ability, we do not repeat the online lectures because that would defeat the purpose of these materials.
Once we have finished this brief discussion, we discuss the day's activity with the class. Each activity requires students to briefly research an assigned question or engage in a group discussion about an important topic. After the class session, students submit a formal response through the learning management system.
Other course sessions help students learn more about how each topic is used in practice. These sessions consist of professional visits, projects (such as the Excel assignments discussed previously), and exams. These activities are staggered to balance the course workload (see Appendix A for an example course schedule). We typically take 5–10 min to introduce each activity by mimicking the conversation students might have with a future supervisor who was giving them instructions on a project. When students are "new hires" at the beginning of the semester, we provide quite a bit of guidance, even walking through the first assignment with the students step‐by‐step; by the end of the semester, we provide fewer steps.
While students are working on their projects, we encourage them to ask questions of each other and of us. To facilitate these conversations, we move around the room and interact with students while they are working. Our goal in each interaction is to provide encouragement and guidance using the Socratic method and to check figures to help them complete the activity.
Finally, we dedicate about two office hours a week to helping students in our class. With class sizes of about 45 students, this typically provides more than enough time to help students with both general questions and questions about specific assignments.
Using effective data
We were careful to find high‐quality data sources that our students could work with and that we could use for in‐class examples. We also wanted data that would not add costs to our students because the cost of course materials is often a challenge. Even with those constraints, there are many suitable data sets available. We start the semester using textbook data sets; they are easily accessible and conceptualized. As we move into more complex examples, we need a large and sufficiently complex database to give students a sense of working with a production database. We evaluated Microsoft's Northwind and AdventureWorks2014 databases and the AdventureWorksDW2014 data warehouse (Rytlewski et al., 2022). AdventureWorks (AW) has proven to work well as a component in an active‐learning course (Mitri, 2015), and Northwind has also been used successfully used to teach advanced database skills (Dyer & Rogers, 2015).
We chose to use the AW databases for four reasons. First, AW's structure provides an integrated functional view of an organization's data that reflects Porter's value chain activities (Porter, 1985). Such a configuration demonstrates a holistic perspective of how data flow across different business functions through an integrated set of processes, making the organization more responsive to changes.
Second, AW has a multiple‐schema database with enough data to replicate a production database reasonably. We use a relational database management system (RDBMS) because such systems are where our graduates will likely access essential structured and unstructured data in practice. Its size shows students that they cannot answer all questions using enumeration, but it is not so large that a Cartesian product brings a virtual desktop infrastructure session to a stop.
Third, an AW 2008 schema diagram also works well despite missing some of the changes found in the 2014 version. The discrepancies provide an excellent example of what often happens in industry; that is, due to organic database changes, the database diagram documentation usually lags production database changes.
Fourth, Microsoft provides detailed instructions for clustering, decision trees, and naive Bayes data‐mining techniques, making it easier for students to use a complex user interface to complete their assignments. While we designed our course using AdventureWorkDW2014, instructors might consider using the most recent database version when designing their course projects. All the AdventureWorks databases are found at the referenced link (Rytlewski et al., 2022).
Using important tools
Analytics combines computer science and statistics, but tool quality allows much of the computer science side to be abstracted away by business students. We use Microsoft software at the undergraduate level because of our employer feedback and student familiarity. For example, employers universally cite Excel as an essential skill (Aboujaoude & Feghali, 2017; Nasir et al., 2020), and we use Excel throughout the required quantitative courses in the college curriculum. In addition to Excel, we introduced the students to Microsoft Access, ERDPlus, SQL Server, SQL Server Management Studio, Power BI, and SQL Server Analysis Services. We introduce Microsoft Access before using SQL Server for several reasons. First, its user interface resembles other products from the Microsoft Office suite, allowing students to focus on the task at hand by lowering entry barriers. Second, the transition from using Excel to using Access is straightforward given the integration level between the two. Third, it requires only a single file to create and store the database. Finally, creating table relationships is fairly uncomplicated. Using Access reduces students' anxiety about using the new software platform, given that many had no prior experience with command‐line interfaces. While Access is neither as versatile nor powerful as SQL Server is, it works well as an intermediate step facilitating the transition to an enterprise‐class RDBMS. The Microsoft suite is the software most frequently used by employers, and our information technology staff supports SQL Server. SQL is central to the effective retrieval of data because organizations store data primarily in relational databases (Chen et al., 2012). We use ERDPlus for database modeling because it supports entity relationship diagrams, relational schemas, and star schemas.
Assessing student performance
We have designed course assessment activities to meet AACSB requirements and, more importantly, to ensure we are helping our students accomplish each of our course objectives. Our first objective, to define the data economy, is perhaps the smallest piece of the course. It was assessed using an open‐book quiz and again through multiple‐choice questions on the first exam. Overall, this component makes up only 3% of the course grade.
The second objective, to explain ethical decision‐making, is assessed using an ethics case that comprises scenarios of misusing data, misrepresenting data, or data privacy issues. Students must evaluate the ethical implications of the case and apply ethical principles to address the case issues. This component, assessed using a standardized rubric, makes up 5% of the course grade.
The third objective, demonstrating how to acquire and prepare data, use data mining, and present results, is perhaps the most important of the three; it covers the majority of the course material. This objective is assessed via open‐book quizzes, homework, and exams. As a group, these activities make up the balance of the course grade. A breakdown of our course grades is available in Table 3.
3 TABLEGrade breakdown
Evidence of efficacy
Anecdotally, we saw increased class participation and read fewer complaints about course material as soon as we implemented these changes. In addition, we have also observed an improvement in course metrics.
AACSB assurance of learning
The effectiveness of the change can be demonstrated by the assessment outcomes we use to comply with the AACSB requirements for assurance of learning (AoL) processes (AACSB, 2020). We assessed these goals using the above assignments. However, instructors could address other data analytics goals through multiple‐choice questions, written responses, homework, and projects. Our results are shown in Table 4.
4 TABLEAssurance of learning
Employer perceptions
School of Business advisory council members, employers, recruiters, and alumni reacted positively to the changes. We asked 25 of our employers whether they had hired employees since 2019 and, if so, how did their analytics skills compared to earlier hires. Specifically, the question was, "How do you rate the analytics skills of our 2019 or more recent graduates compared to earlier employees? 1: much worse, 2: worse, 3: about the same, 4: better, 5: much better." Eight responded, with a mean of 4.125 and a standard deviation of 0.835. An example comment from a board member who works for one of the largest transportation companies in the country said, "We are ecstatic to hire graduates with SQL skills. Data support all of our important decisions, so having an individual who can analyze the data and harvest it is extremely valuable."
Goal realization
In addition to increased satisfaction from stakeholders, the redesigned course helped us achieve other goals. First, the new design effectively complements the other data analytics courses offered in our institution's analytics major and other majors. Second, we provide students with the hands‐on practical experience employers often seek. Third, our students have more in‐depth technical expertise. The change in the course required that we sacrifice the breadth of a more traditional course. Thus, our students may not be familiar with basic networking, systems analysis, design, or IT project management concepts. However, we believe this trade‐off better equips students for current and future job market requirements.
To assess the efficacy of the shift from a traditional survey IS course to a data analytics course, we examined the course evaluation data before and after we made the change. The results in each of the IDEA evaluation categories (
Figure 1 presents the IDEA student evaluations for the Summary Evaluation category (an average of the results from the other reported categories) for our IS course from Fall 2011 to Fall 2020. The blue circles (from Fall 2011 to Spring 2016) are from the semesters when the course was taught using the traditional information systems (IS) emphasis, and the red squares (from Fall 2016 to Fall 2020) are from the semester when the course was taught using the new data orientation (DO) emphasis. On average, 70% of students completed evaluations. The highest response rate was 36 out of 39 (92.3%) in Fall 2018, and the lowest rate was 9 out of 29 (31%) in Summer 2016. Overall, 1209 out of 1734 students completed the course evaluations over the period presented.
As shown in Figure 1, the overall positive student evaluations significantly increased when using the new method. Overall, the average summary score increased from 36.4 to 53.2, an improvement of a full ranking category in the IDEA system.
CONCLUSION
Reorienting a required information systems course to emphasize analytics has multiple benefits. First, students learn the concepts of relational database technology, business intelligence, and analytics. Second, they acquire skills and practice using standard tools to support business decision‐making. Third, students apply critical thinking in the mining phase of each acquire–prepare–mine–present cycle. Fourth, students understand the importance of data and analytics to their careers. Fifth, covering fewer topics in greater depth increases instructor and student satisfaction. Sixth, students have skills they can use when they begin work as business professionals. The benefits of these changes justified our investment in reorienting the course, since it now provides students with skills needed to understand, retrieve SI, collect, and interpret information in a data‐driven society.
APPENDIX A COURSE STRUCTURE
The following table summarizes our course plan for the semester by week. We have included the preparation assignment, in‐class activities, and follow‐up assignments. Specific deadlines and dates have not been included because they change from semester to semester based on student needs, university holidays, instructor preferences, etc. Please note that the chapter and appendix reference in this table refer to our course textbook,
GRAPH: Supplementary Information
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By Thomas Tiahrt; Bartlomiej Hanus and Jason C. Porter
Reported by Author; Author; Author
Thomas Tiahrt is the POET professor of Business Analytics in the Beacom School of Business at the University of South Dakota. He earned his PhD in computational science and statistics and his MA in computer science from the University of South Dakota. His research has appeared in Omega, The International Journal of Management Science, Business Education Innovation Journal, and Decision Support Systems.
Bartlomiej Hanus is an associate professor of Decision Sciences in the Beacom School of Business at the University of South Dakota. He received his PhD degree in business computer information systems from the University of North Texas. His primary research interests revolve around information security.
Jason Porter, PhD, is a scholarly associate professor of accounting at Washington State University's Carson College of Business. He earned his PhD at the University of Georgia and his master of accountancy from Brigham Young University. Over the past 16 years, he has taught courses in financial accounting, cost accounting, accounting theory, and accounting ethics, as well as classes on accounting for nonaccountants. Jason has over 30 publications appearing in journals such as The CPA Journal, Journal of Business Ethics Education, Strategic Finance, Issues in Accounting Education, Radiology Management, Business Education Innovation Journal, and Journal of Accounting Education.