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Computer Laboratory Workshops as Learning Environments for University Business Statistics: Validation of Questionnaires

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Title: Computer Laboratory Workshops as Learning Environments for University Business Statistics: Validation of Questionnaires
Language: English
Authors: Nguyen-Newby, Thuyuyen H., Fraser, Barry J. (ORCID 0000-0003-1026-9495)
Source: Learning Environments Research. Oct 2021 24(3):389-407.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 19
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Computer Centers, Business Administration Education, Statistics Education, Educational Environment, Questionnaires, Test Validity, Student Attitudes, College Students, Foreign Countries, Workshops
Geographic Terms: United Kingdom
DOI: 10.1007/s10984-020-09324-z
ISSN: 1387-1579
Abstract: Research on learning environments at the higher-education level has been quite sparse compared with studies at other educational levels. Because statistics is perceived as a difficult subject across disciplines, it suffers from low passing rates in many universities. This study involved validating questionnaires for assessing the psychosocial environment and student attitudes associated with learning business statistics in computing laboratory workshops. The Business Statistics Computer Learning Environment Inventory (BSCLEI) and Attitude to Business Analytics instrument were validated with 275 students enrolled across various business degree programs in the United Kingdom over two academic years. Various data analyses (including exploratory and confirmatory factor analyses) supported the validity of these two questionnaires, thereby paving the way for their future use in research and practical applications relevant to learning environments in higher-education statistics workshop classrooms.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1310059
Database: ERIC
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  Value: <anid>AN0152424141;oje01oct.21;2021Sep16.01:23;v2.2.500</anid> <title id="AN0152424141-1">Computer laboratory workshops as learning environments for university business statistics: validation of questionnaires </title> <p>Research on learning environments at the higher-education level has been quite sparse compared with studies at other educational levels. Because statistics is perceived as a difficult subject across disciplines, it suffers from low passing rates in many universities. This study involved validating questionnaires for assessing the psychosocial environment and student attitudes associated with learning business statistics in computing laboratory workshops. The Business Statistics Computer Learning Environment Inventory (BSCLEI) and Attitude to Business Analytics instrument were validated with 275 students enrolled across various business degree programs in the United Kingdom over two academic years. Various data analyses (including exploratory and confirmatory factor analyses) supported the validity of these two questionnaires, thereby paving the way for their future use in research and practical applications relevant to learning environments in higher-education statistics workshop classrooms.</p> <p>Keywords: Attitudes; Attitude–environment associations; Business statistics; Computer laboratory workshops; Higher education; Learning environment</p> <hd id="AN0152424141-2">Introduction</hd> <p>The great majority of learning environment studies have been carried out in secondary and primary schools, with a much smaller number at college and university levels (Alansari and Rubie-Davies [<reflink idref="bib2" id="ref1">2</reflink>]). Most of the instruments developed for higher education appear to be for specialist settings, such as university science laboratory classrooms (Fraser et al. [<reflink idref="bib20" id="ref2">20</reflink>]), with the exception of the College and University Classroom Environment Inventory (CUCEI, Fraser and Treagust [<reflink idref="bib24" id="ref3">24</reflink>]; Hasan and Fraser [<reflink idref="bib31" id="ref4">31</reflink>]) for seminar environments involving up to 30 students rather than large lecture environments.</p> <p>Statistics is a subject that has wide application in a large number of fields including health care, pharmaceuticals, actuarial science, psychology, education, engineering and business. Because of its wide applicability, understanding statistics is viewed as being useful in every degree program, most careers and many aspects of everyday life. Guha and Shen ([<reflink idref="bib28" id="ref5">28</reflink>]) argue that statistics is more relevant than calculus: "Statistics is necessary for analyzing news reports, understanding scientific results, and quantifying any phenomenon. Calculus simply lacks this universal applicability." As a consequence, statistics is taught across a range of disciplines and at various levels from high school through to doctoral programs. Apart from specialist mathematical statistics courses, the foci in statistics courses are analysing and summarising large amounts of data and interpreting the results. Because teaching statistics in this context means that it is a service subject that is often taught by specialist statisticians, many students appreciate neither why they are studying it nor its relevance to their major field of study (Petocz and Reid [<reflink idref="bib57" id="ref6">57</reflink>]).With many students not appreciating the importance of statistics, they tend to dismiss it as dull and boring and only enroll in it because it is compulsory (Gordon [<reflink idref="bib27" id="ref7">27</reflink>]).</p> <p>Statistics can be applied to many aspects of business and used in tasks such as tracking revenue and costs, forecasting sales, controlling inventory and determining the success of a marketing strategy. Although statistics has been a required course in business degree programs for many years, it has become increasingly important in recent years as the application of statistical techniques to business problems has become more frequent, particularly in the use of 'big data' (Manyika and Chui [<reflink idref="bib40" id="ref8">40</reflink>]). Because university business schools have long recognised the importance of statistics in business education, at least one statistics course is mandatory at both the undergraduate and graduate levels, especially for those programs accredited by bodies such as the Association to Advance Collegiate Schools of Business (AACSB). However, despite the evidence, a large number of business students do not see the relevance of statistics to their chosen major (Petocz and Reid [<reflink idref="bib57" id="ref9">57</reflink>]). For them, statistics is a service subject which apparently is unrelated to their major field of study and is just something they need to pass. In addition, because business statistics courses have a reputation for being very hard, whether deservedly or not, some students enroll in such courses with the belief that they are likely to fail. As a result of these attitudes, many students put in minimum effort and fail to achieve a passing grade. This is reflected in statistics courses having relatively high failure rates (often greater than 25%) and reputations as 'bottle-necks' that delay or even prevent graduation.</p> <p>Several studies have shown an association between achievement and attitudes in a number of subjects including science (Freedman [<reflink idref="bib26" id="ref10">26</reflink>]), mathematics (Hemmings et al. [<reflink idref="bib32" id="ref11">32</reflink>]; Ma and Kishor [<reflink idref="bib39" id="ref12">39</reflink>]), business computing (Newby and Fisher [<reflink idref="bib48" id="ref13">48</reflink>]) and statistics (Onwuegbuzie [<reflink idref="bib51" id="ref14">51</reflink>]; Onwuegbuzie and Wilson [<reflink idref="bib55" id="ref15">55</reflink>]; Pan and Tang [<reflink idref="bib56" id="ref16">56</reflink>]). In educational settings, one of the most important influences on student attitudes is the classroom learning environment (Fraser [<reflink idref="bib19" id="ref17">19</reflink>]). Relationships between aspects of the learning environment and attitudes were reported for mathematics by B. Taylor and Fraser ([<reflink idref="bib62" id="ref18">62</reflink>]), for business computing by Newby and Fisher ([<reflink idref="bib48" id="ref19">48</reflink>]) and for a business statistics course with classes of up to 40 students by Nguyen et al. ([<reflink idref="bib50" id="ref20">50</reflink>]).</p> <p>This article describes the validation of two existing instruments in a university setting where students attend large lectures together with small computer laboratory-based workshops. One instrument measures students' perceptions of the learning environment of their workshop sessions and the other measures student attitudes to business statistics courses.</p> <p>This research is distinctive because of its foci on the higher-education level and on the teaching area of statistics. Additionally, the instructional approach, involving lectures plus structured and informal workshops, is quite unusual in the teaching of university courses on business statistics.</p> <hd id="AN0152424141-3">Literature review</hd> <p></p> <hd id="AN0152424141-4">Learning environments</hd> <p>Dating back over 50 years, learning environments research concerns social and psychological aspects of learning, determinants of classroom and school environments, and their effects on attitudes to learning and academic achievement (Fraser [<reflink idref="bib18" id="ref21">18</reflink>], [<reflink idref="bib19" id="ref22">19</reflink>]; Fraser and Walberg [<reflink idref="bib25" id="ref23">25</reflink>]; Haertel et al. [<reflink idref="bib29" id="ref24">29</reflink>]). Objective measures of classroom environments were first used in two research programs that started in the US in the late 1960s (Anderson and Walberg [<reflink idref="bib6" id="ref25">6</reflink>]; Anderson et al. [<reflink idref="bib7" id="ref26">7</reflink>], Trickett and Moos [<reflink idref="bib64" id="ref27">64</reflink>]). Past research has revealed a positive relationship between the classroom setting and academic performance (Dorman and Fraser [<reflink idref="bib14" id="ref28">14</reflink>]). Different classes have different characteristics depending upon how students interact with each other, the teacher and their environment. Many researchers have concentrated on the link between students' perceptions of their classroom environment and attitudinal and cognitive outcomes. Anderson and Walberg ([<reflink idref="bib6" id="ref29">6</reflink>]) identified how the learning environment acts as a catalyst for the student's learning process by providing a path for learning.</p> <p>Trickett and Moos ([<reflink idref="bib64" id="ref30">64</reflink>]) studied social climate scales in different settings. Moos ([<reflink idref="bib45" id="ref31">45</reflink>]) identified the dimensions of 'relationship', personal development' and 'systems maintenance and system change' as the requirements of a suitable environment, and this was later presented in a theoretical framework in which the ambience of the classroom is directly affected by the characteristics of the organisation (Moos [<reflink idref="bib46" id="ref32">46</reflink>]). All three dimensions are defined to some extent by the context of the institution and the specific classroom situation, relating separately to the students and to the teachers. Fraser and Treagust ([<reflink idref="bib24" id="ref33">24</reflink>]) came to the important conclusion that a well-established environment for teaching can be a precursor to positive educational outcomes, rather than simply being worthwhile in itself, and student satisfaction is enhanced in an environment characterised by cohesiveness and tasks with a clear purpose. Haertel et al. ([<reflink idref="bib29" id="ref34">29</reflink>]) meta-analysis involving 17,805 students identified the learning environment as the driver of enhanced student outcomes (especially achievement) in learning and teaching.</p> <p>Research into learning environments has led to the development of a number of general-purpose and specialist learning environment instruments (Fraser [<reflink idref="bib17" id="ref35">17</reflink>], [<reflink idref="bib18" id="ref36">18</reflink>], [<reflink idref="bib19" id="ref37">19</reflink>]). The main requirements of these instruments are that they measure appropriate aspects of the learning environment and are short/economical enough to minimise student fatigue and therefore encourage students to complete them. Therefore, those environment factors that are most relevant to the challenges of the general classroom are identified. Of all the instruments available, the one used most frequently over the last two decades is the What Is Happening In this Class? (WIHIC, Aldridge et al. [<reflink idref="bib4" id="ref38">4</reflink>]; Fraser, McRobbie and Fisher [<reflink idref="bib22" id="ref39">22</reflink>]), which has achieved what Dorman ([<reflink idref="bib13" id="ref40">13</reflink>]) termed 'almost band wagon status'. The WIHIC's seven eight-item scales are Student Cohesiveness, Teacher Support, Involvement, Investigation, Task Orientation, Cooperation and Equity. In addition to general classroom instruments, a number of specialist classroom questionnaires have been developed and applied in environments such as science laboratories, computer laboratories, language laboratories, distance-learning settings, constructivist-oriented classrooms, web-based environments and business statistics workshops (Afari et al. [<reflink idref="bib1" id="ref41">1</reflink>]; Aldridge et al. [<reflink idref="bib5" id="ref42">5</reflink>]; Fraser et al. [<reflink idref="bib23" id="ref43">23</reflink>]; Newby and Fisher [<reflink idref="bib47" id="ref44">47</reflink>]; Nguyen et al. [<reflink idref="bib49" id="ref45">49</reflink>]; Skordi and Fraser [<reflink idref="bib60" id="ref46">60</reflink>]; Taylor et al. [<reflink idref="bib63" id="ref47">63</reflink>]; Wolf and Fraser [<reflink idref="bib66" id="ref48">66</reflink>]).</p> <p>With growth in the use of information technology in education over the past three decades, instruments have been developed to measure aspects of computer laboratories (e.g. Newby and Fisher [<reflink idref="bib47" id="ref49">47</reflink>], [<reflink idref="bib48" id="ref50">48</reflink>]) and technology-integrated science classrooms (Wu et al. [<reflink idref="bib67" id="ref51">67</reflink>]). The widespread use of this technology has given rise to 'technology-rich learning environments' which led to the development of the Technology-Rich Outcomes-Focused Learning Environments Inventory (TROFLEI, Aldridge and Fraser [<reflink idref="bib3" id="ref52">3</reflink>]; Dorman and Fraser [<reflink idref="bib14" id="ref53">14</reflink>]), which is based on the WIHIC but has the extra scales Computer Usage, Differentiation and Young Adult Ethos. Using a causal model, Dorman and Fraser ([<reflink idref="bib14" id="ref54">14</reflink>]) demonstrated relationships between antecedents (age, grade and computer access), the learning environment and affective outcomes. When Fraser and Kahle ([<reflink idref="bib21" id="ref55">21</reflink>]) examined the role of the classroom environment as a determinant of nearly 7000 science and mathematics students' achievement and attitudes, the class environment enhanced students' attitudes more than the peer or home environment.</p> <hd id="AN0152424141-5">Student attitudes</hd> <p>Attitudes towards people, using computers, learning something new, using statistics and learning statistical techniques all have cognitive, affective and behavioural aspects (McGuire [<reflink idref="bib43" id="ref56">43</reflink>]). What an individual believes is classed as a cognitive component and includes beliefs such as 'statistical analysis can contribute to the success of a business'. An individual's feelings about an object are classed as affective components and are based on emotions such as anxiety or enjoyment (e.g. 'working with statistics makes me nervous' or 'I enjoy analysing data'). A behavioural component is a predisposition to act based on cognitive and affective components (e.g. procrastination when undertaking a statistics course). If individuals believe that a course is difficult, they worry about having to do it in case they get a low grade or even fail. To avoid this possibility, they might delay enrolling in the course for as long as possible. To have feelings about a situation, people must believe something about it (Kek and Huijser [<reflink idref="bib36" id="ref57">36</reflink>]) and these feelings lead an individual to behave in a certain way. If the feelings are positive, individuals have positive attitudes towards the situation and behave accordingly, and vice versa (McGuire [<reflink idref="bib43" id="ref58">43</reflink>]). Because belief, affect and behaviour often exhibit a feedback loop (Hsu et al. [<reflink idref="bib33" id="ref59">33</reflink>]), negative beliefs and associated negative emotions can lead to behaviour that reinforces the negative beliefs and makes the situation worse. This is particularly pertinent to learning because, if a student has a negative attitude towards a subject, more exposure to the topic is likely to make that attitude worse.</p> <p>Of the attitudinal outcomes regarding learning statistics, the one that has received the most attention is anxiety. It has been defined as a feeling when taking a statistics course or doing statistical analysis (Cruise et al. [<reflink idref="bib12" id="ref60">12</reflink>]), as involving worry, tension and stress in students taking a statistics course (Zeidner [<reflink idref="bib69" id="ref61">69</reflink>]), or simply as a tension when a student encounters statistics in any form or at any level (Onwuegbuzie et al. [<reflink idref="bib53" id="ref62">53</reflink>]). There are similarities between statistics anxiety and mathematics anxiety, because both types are related to the manipulation of numbers and symbols. However, in statistics, students are required to have opinions about what the results actually mean (Williams [<reflink idref="bib65" id="ref63">65</reflink>]).</p> <p>Statistics anxiety has been found to corelate negatively with performance (Lalonde and Gardner [<reflink idref="bib38" id="ref64">38</reflink>]; Onwuegbuzie and Seaman [<reflink idref="bib54" id="ref65">54</reflink>]; Pan and Tang [<reflink idref="bib56" id="ref66">56</reflink>]; Williams [<reflink idref="bib65" id="ref67">65</reflink>]; Zanakis and Valenzi [<reflink idref="bib68" id="ref68">68</reflink>]) and to lead to procrastination, resulting in students not enrolling in a required statistics course until the last possible moment in their degree program (Onwuegbuzie [<reflink idref="bib52" id="ref69">52</reflink>]) and therefore being unprepared for other courses that require the use of statistical techniques. As a potentially complicating factor, most statistics courses now have a computer laboratory component that requires students to be competent in using software such as Microsoft Excel or SPSS (Hsu et al. [<reflink idref="bib33" id="ref70">33</reflink>]) to be successful in the course. For a student already suffering from statistics anxiety, adding the need for computer competency could increase overall anxiety because using a computer is known to be associated with anxiety which in turn is related negatively with course achievement (Marcoulides [<reflink idref="bib41" id="ref71">41</reflink>]).</p> <p>Various instruments have been developed for measuring different aspects of attitude in several disciplines and settings such as mathematics (Hannula [<reflink idref="bib30" id="ref72">30</reflink>]), science (Fraser [<reflink idref="bib16" id="ref73">16</reflink>]) and computer usage (Newby and Fisher [<reflink idref="bib47" id="ref74">47</reflink>]). The numerous instruments for measuring students' attitudes toward statistics reviewed by Ramirez et al. ([<reflink idref="bib58" id="ref75">58</reflink>]) include single-construct measures, such as the Self-Efficacy to Learn Statistics (Finney and Schraw [<reflink idref="bib15" id="ref76">15</reflink>]), and six-construct questionnaires, such as the Statistical Anxiety Rating Scale (STARS) (Cruise et al. [<reflink idref="bib12" id="ref77">12</reflink>]) and Survey of Attitudes Towards Statistics-36 (Schau [<reflink idref="bib59" id="ref78">59</reflink>]). The STARS has been used in the majority of past studies of attitudes towards statistics. STARS has four scales that measure an aspect of anxiety or fear (namely, interpretation anxiety, test anxiety, fear of asking for help, and fear of the instructor), one scale assessing the worth of statistics (perceptions of the usefulness of statistics) and one computational self-concept scale (beliefs in ability to perform required calculations). More recently, the Attitude toward Business Statistics was developed (Nguyen, Newby & Skordi [<reflink idref="bib50" id="ref79">50</reflink>]) with two scales measuring affective components (anxiety; enjoyment) and two scales assessing cognitive components (usefulness of statistics in the degree program; and usefulness of statistics in general, particularly in running a business).</p> <p>This article brings together the two areas (learning environments and student attitudes) reviewed above by reporting a study involving the validation of two economical and generally-applicable instruments for assessing these two constructs in the specific setting of statistics courses in higher education.</p> <hd id="AN0152424141-6">Methods</hd> <p></p> <hd id="AN0152424141-7">Sample</hd> <p>The participants in this study were 275 undergraduate students enrolled at a Business School at a university in the North East of England and undertaking a mandatory undergraduate course on quantitative methods and statistical analysis. The majority of students were between the ages of 18 and 20 years (85.8%); 54.9% were male and 45.1% female; and 80.7% were from the UK, 5.8% were from the European Union and 13.5% were international students. In terms of majors, 20.7% were Accounting, 15.6% Human resources, 12.7% Finance, 10.9% Travel and Tourism and the rest were evenly distributed across Economics, International Business, Management, Marketing and General Business.</p> <hd id="AN0152424141-8">Teaching module and workshops</hd> <p>The teaching module covers the fundamentals of business analytics and the interpretation of results for a general business audience. The teaching spans the 24 weeks of the first academic year (12 weeks in each term). Students have a one-hour lecture (up to 260 students) every two weeks and a one-hour computer laboratory workshop every week (around 18–24 students). Lectures are devoted to theory and the use of the statistical techniques in business applications. Because there were over 700 students taking this module, the same lecture was delivered three times by the lecturer in charge of the module. The computer-based workshops allow practical application of these techniques and involve the use of Microsoft Excel. Students are given exercises that require performing analyses and interpreting the results. In this environment, students work closely with their instructors, have opportunities to ask questions and receive individual formative feedback on their activities. These workshops are run by academic staff (not teaching assistants) who usually are different from those who deliver the lectures.</p> <p>These workshops, which are quite structured and informal, provide students with hands-on practice in applying statistical techniques covered in the lectures to real-world problems. For each workshop, the course teaching team develops a set of exercises, with the instructor running the workshop providing students with clear instructions and expectations for each activity at the beginning of the session. After that, workshops become informal, with students working together or individually and receiving assistance from the instructor. During workshops, instructors identify common student problems, which are discussed with students at the end of the session and also relayed back to the teaching team to address as part of course re-development and evaluation. No new material is introduced during these workshops.</p> <p>The assessment for this module consists of three components, namely, 15% for a test in week 8 of Semester 1, 15% for a test in week 8 of Semester 2, and 70% for a final traditional closed-book examination at the end of the second semester. The examination is proctored by university staff. Examination questions require students to understand problems, undertake calculations, and interpret results in a clear and logical manner that would be understandable by a general business audience. Data for the study were collected from 3 lectures during week 9 of the second term by non-teaching staff. Participation was voluntary.</p> <hd id="AN0152424141-9">Instruments: Business Statistics Computer Laboratory Environment Inventory (BSCLEI) and Attit...</hd> <p>To measure salient aspects of the learning environment, the Business Statistics Computer Laboratory Environment Inventory (BSCLEI, Nguyen, Newby and Skordi [<reflink idref="bib50" id="ref80">50</reflink>]) was used to assess Student Cohesiveness, Integration, Technology Adequacy, Task Orientation, and Involvement. Table 1 provides a scale description and sample item for each BSCLEI construct. The selection of these five scales for this study was based on relevance to the type of classes being investigated, namely, computer workshops in which students were required to apply the techniques learned during lectures. As noted previously, workshops were supervised by the lecturers (not teaching assistants), who provided exercises for the students to work through relatively informally. Given the nature of these workshops, the scales were chosen for the following reasons: Student Cohesiveness was selected because we had observed that students in such classes collaborate with each other; Integration was chosen because the exercises were based on material covered in the lectures; Technology Adequacy was relevant because the exercises were computer based and required appropriate hardware and software; Task Orientation was included because the laboratory exercises are structured and students need to know what they are required to do; and Involvement was selected because students in computer-based classrooms are expected to interact with their instructor and other students while solving the practical exercises.</p> <p>Table 1 Scale description and sample item for each Business Statistics Computer Laboratory Environment Inventory (BSCLEI) construct</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Construct</p></th><th align="left"><p>Description</p></th><th align="left"><p>Sample item</p></th></tr></thead><tbody><tr><td align="left"><p>Student Cohesiveness (SC)</p></td><td align="left"><p>Extent to which students know, help, and are supportive of each other</p></td><td align="left"><p>I get on well with the other students in my workshop sessions</p></td></tr><tr><td align="left"><p>Integration (IN)</p></td><td align="left"><p>Extent to which the workshop activities are integrated with theory classes</p></td><td align="left"><p>I use the theory from my lecture sessions during workshop activities</p></td></tr><tr><td align="left"><p>Technology Adequacy (TA)</p></td><td align="left"><p>Extent to which the hardware and software is adequate for the tasks required</p></td><td align="left"><p>The software available enables me to work on my workshop exercise efficiently</p></td></tr><tr><td align="left"><p>Involvement (IV)</p></td><td align="left"><p>Extent to which students have attentive interest, participate in discussions, do additional work and enjoy the class.</p></td><td align="left"><p>I put effort into what I do in</p><p>the workshop sessions</p></td></tr><tr><td align="left"><p>Task Orientation (TO)</p></td><td align="left"><p>Extent to which it is important to complete activities planned and to stay on the subject matter.</p></td><td align="left"><p>I know exactly what has to be done in our workshop sessions</p></td></tr></tbody></table> </ephtml> </p> <p>To measure students' attitudes, the existing Attitude to Business Statistics (ABS, Nguyen, Newby and Skordi, [<reflink idref="bib50" id="ref81">50</reflink>]) was modified to form the Attitude to Business Analytics (ABA) whose four scales are Anxiety, Enjoyment, Perceived Usefulness of Business Analytics, and Perceived Usefulness in Employability. A scale description and sample item for each ABA construct are given in Table 2. The Anxiety and Enjoyment scales measure negative and positive feelings towards learning and using statistics; these are affective components. Perceived Usefulness and Perceived Employability are cognitive components involving students' beliefs about the need to learn statistics for, respectively, general use (including within the degree program) and within their career.</p> <p>Table 2 Scale description and sample item for each Attitude towards Business Analytics (ABA) construct</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Construct</p></th><th align="left"><p>Description</p></th><th align="left"><p>Sample item</p></th></tr></thead><tbody><tr><td align="left"><p>Anxiety (AX)</p></td><td align="left"><p>Extent to which students feel anxious about business analytics</p></td><td align="left"><p>Working with business analytics makes me very nervous</p></td></tr><tr><td align="left"><p>Enjoyment (EN)</p></td><td align="left"><p>Extent to which students enjoy studying business analytics</p></td><td align="left"><p>I like the challenge of solving business analytics problems</p></td></tr><tr><td align="left"><p>Perceived Usefulness of Business Analytics (PUC)</p></td><td align="left"><p>Extent to which students believe the business analytics is useful</p></td><td align="left"><p>All Business students need a module in business analytics</p></td></tr><tr><td align="left"><p>Perceived Usefulness in Employability (PUE)</p></td><td align="left"><p>Extent to which students believe business analytics will be useful in obtaining future employment</p></td><td align="left"><p>My future career will require knowledge of business analytics</p></td></tr></tbody></table> </ephtml> </p> <p>All BSCLEI and ABA scales have five items and are responded to on a five-point Likert scale (ranging from strongly disagree to strongly agree). Each student's scale mean was calculated for each scale by averaging the five item scores so that the respondent had a score between 1 and 5. The technique of averaging the scores was chosen for its simplicity and because using unit weighting has similar predictive validity to using regression weights (Bobco, Roth, and Buster [<reflink idref="bib8" id="ref82">8</reflink>]; Kline [<reflink idref="bib37" id="ref83">37</reflink>]).</p> <hd id="AN0152424141-10">Instrument-validation procedures</hd> <p>Even though BSLEI and ABA scales were taken from instruments that have been used and validated previously, this statistics course involves a new setting which necessitated carrying out exploratory factor analyses to extract the hypothesised scales (Kline [<reflink idref="bib37" id="ref84">37</reflink>]). Exploratory factor analysis for the BSCLEI involved conducting principal component analysis with varimax rotation using SPSS 24 (because it was believed that BSCLEI factors are only weakly correlated). Principal component analysis for the ABA involved direct oblimin rotation with δ = 0 (because it was believed that ABA factors are correlated and oblique rotation can produce clearer axes; Tabachnick and Fidell [<reflink idref="bib61" id="ref85">61</reflink>]). Next, confirmatory factor analysis (using AMOS 24), as well as reliability and discriminant validity analyses were carried out for both the BSCLEI and ABA. Finally, the predictive validity of the BSCLEI was checked in terms of its associations with student attitudes.</p> <hd id="AN0152424141-11">Results</hd> <p></p> <hd id="AN0152424141-12">Principal component analysis</hd> <p>For both the BSCLEI and ABA, exploratory factor analysis was undertaken via principal component analysis. BSCLEI data yielded a high value of 0.826 for the Kaiser–Meyer–Olkin (KMO) sampling adequacy measurement (Kaiser [<reflink idref="bib35" id="ref86">35</reflink>]), indicating that the matrix was factorable. Using eigenvalues being greater than 1 and factor loadings being at least 0.4 as cut-off criteria, five factors were extracted for the BSCLEI. These five factors explained 63.40% of the variance, which is satisfactory according to Kline ([<reflink idref="bib37" id="ref87">37</reflink>]). The principal component analysis, reported in Table 3 with loadings of less than 0.4 not reported, shows that all hypothesised items loaded onto their a priori scales. Factor loadings were between 0.695 and 0.783 for Student Cohesiveness, between 0.586 and 0.838 for Integration, between 0.627 and 0.783 for Technology Adequacy, between 0.611 and 0.783 for Involvement, and between 0.644 and 0.748 for Task Orientation.</p> <p>Table 3 Factor loadings of rotated component matrix for Business Statistics Computer Laboratory Environment Inventory (BSCLEI)</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Component</p></th><th align="left" colspan="5"><p>Factor loadings</p></th></tr><tr><th align="left" /><th align="left"><p>Factor 1</p></th><th align="left"><p>Factor 2</p></th><th align="left"><p>Factor 3</p></th><th align="left"><p>Factor 4</p></th><th align="left"><p>Factor 5</p></th></tr></thead><tbody><tr><td align="left"><p>SC1</p></td><td align="left" /><td char="." align="char"><p>0.695</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>SC3</p></td><td align="left" /><td char="." align="char"><p>0.783</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>SC4</p></td><td align="left" /><td char="." align="char"><p>0.782</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>SC5</p></td><td align="left" /><td char="." align="char"><p>0.723</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>SC6</p></td><td align="left" /><td char="." align="char"><p>0.769</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p><underline>IN1</underline></p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.727</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p><underline>IN2</underline></p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.838</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>IN4</p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.586</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>IN5</p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.693</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>IN6</p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.679</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>TA2</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.760</p></td><td align="left" /></tr><tr><td align="left"><p>TA3</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.783</p></td><td align="left" /></tr><tr><td align="left"><p>TA4</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.670</p></td><td align="left" /></tr><tr><td align="left"><p>TA5</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.627</p></td><td align="left" /></tr><tr><td align="left"><p>TA6</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.730</p></td><td align="left" /></tr><tr><td align="left"><p><underline>INV1</underline></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.611</p></td></tr><tr><td align="left"><p>INV2</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.752</p></td></tr><tr><td align="left"><p>INV3</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.783</p></td></tr><tr><td align="left"><p>INV4</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.714</p></td></tr><tr><td align="left"><p><underline>INV5</underline></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.771</p></td></tr><tr><td align="left"><p>TO1</p></td><td char="." align="char"><p>0.719</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>TO2</p></td><td char="." align="char"><p>0.748</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>TO3</p></td><td char="." align="char"><p>0.710</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p><underline>TO5</underline></p></td><td char="." align="char"><p>0.726</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>TO6</p></td><td char="." align="char"><p>0.664</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>Extraction method: principal component analysis; rotation method: varimax with Kaiser normalization; rotation converged in 6 iterations <emph>N </emph>= 275, SC Student Cohesiveness, IN Integration, TA Technology Adequacy, INV Involvement, TO Task Orientation</p> <p>Principal component analysis for the ABA revealed a high KMO value of 0.931, indicating that the matrix is factorable. Using eigenvalues greater than 1 as the criterion, four components explaining 75.89% of the variance were extracted. Factor loadings for the ABA are shown in Table 4, with loadings less than 0.4 not reported. All items loaded on their a priori factors. Factor loadings ranged from 0.732 to 0.792 for Anxiety, from 0.622 to 0.922 for Enjoyment, from 0.661 to 0.843 for Perceived Usefulness and from 0.749 to 0.812 for Perceived Employability.</p> <p>Table 4 Factor loadings for rotated component matrix for Attitude to Business Analytics (ABA) constructs</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Component</p></th><th align="left" colspan="4"><p>Factor loadings</p></th></tr><tr><th align="left" /><th align="left"><p>Factor 1</p></th><th align="left"><p>Factor 2</p></th><th align="left"><p>Factor 3</p></th><th align="left"><p>Factor 4</p></th></tr></thead><tbody><tr><td align="left"><p>AX2</p></td><td align="left" /><td char="." align="char"><p>0.732</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>AX3</p></td><td align="left" /><td char="." align="char"><p>0.776</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>AX4</p></td><td align="left" /><td char="." align="char"><p>0.792</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>AX5</p></td><td align="left" /><td char="." align="char"><p>0.745</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>AX6</p></td><td align="left" /><td char="." align="char"><p>0.770</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>EN1</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.854</p></td></tr><tr><td align="left"><p>EN2</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.922</p></td></tr><tr><td align="left"><p>EN3</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.622</p></td></tr><tr><td align="left"><p>EN4</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.651</p></td></tr><tr><td align="left"><p>EN6</p></td><td align="left" /><td align="left" /><td align="left" /><td char="." align="char"><p>0.809</p></td></tr><tr><td align="left"><p>PUC2</p></td><td char="." align="char"><p>0.817</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>PUC3</p></td><td char="." align="char"><p>0.800</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>PUC4</p></td><td char="." align="char"><p>0.843</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>PUC5</p></td><td char="." align="char"><p>0.661</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>PUC6</p></td><td char="." align="char"><p>0.778</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>PUE1</p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.812</p></td><td align="left" /></tr><tr><td align="left"><p>PUE2</p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.757</p></td><td align="left" /></tr><tr><td align="left"><p>PUE4</p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.798</p></td><td align="left" /></tr><tr><td align="left"><p>PUE5</p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.802</p></td><td align="left" /></tr><tr><td align="left"><p>PUE6</p></td><td align="left" /><td align="left" /><td char="." align="char"><p>0.749</p></td><td align="left" /></tr></tbody></table> </ephtml> </p> <p>Extraction method: principal component analysis. Rotation method: oblimin with Kaiser normalisation. Rotation converged in 9 iterations <emph>N </emph>= 275, AX Anxiety, EN Enjoyment, PUC Perceived Usefulness of Business Analytics, PUE Perceived Usefulness in Employability</p> <hd id="AN0152424141-13">Confirmatory factor analysis</hd> <p>When confirmatory analyses were carried out for the measurement model for the BSCLEI shown in Fig. 1, regression coefficients for this model ranged from 0.555 to 0.794 and were statistically significant <emph>(p</emph> < 0.001). These coefficients and the satisfactory fit indices shown in Table 5 indicate that the overall model represents relationships in the data very well. When confirmatory analysis also was carried out for the measurement model of the ABA shown in Fig. 2, regression coefficients ranged from 0.657 to 0.923 and all were statistically significant (<emph>p</emph> < 0.001). These regression coefficients and satisfactory fit indices (Table 5) confirm that the overall model represents relationships in the data very well (Byrne [<reflink idref="bib9" id="ref88">9</reflink>]; Hu and Bentler [<reflink idref="bib34" id="ref89">34</reflink>]; Marcoulides and Hershberger [<reflink idref="bib42" id="ref90">42</reflink>]).</p> <p>Graph: Fig. 1 Measurement model for Business Statistics Computer Learning Environment Inventory (BSCLEI)</p> <p>Table 5 Goodness-of-fit indices for confirmatory factor analysis for measurement models for Business Statistics Computer Learning Environment Inventory (BSCLEI) and Attitude to Business Analytics (ABA) questionnaire</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Goodness-of-fit index</p></th><th align="left"><p>BSCLEI</p></th><th align="left"><p>ABA</p></th></tr></thead><tbody><tr><td align="left"><p>χ<sup>2</sup></p></td><td char="." align="char"><p>42.71</p></td><td char="." align="char"><p>45.94</p></td></tr><tr><td align="left"><p>CMIN/<italic>df</italic></p></td><td char="." align="char"><p>1.42</p></td><td char="." align="char"><p>1.48</p></td></tr><tr><td align="left"><p><italic>p</italic></p></td><td char="." align="char"><p>0.06</p></td><td char="." align="char"><p>0.04</p></td></tr><tr><td align="left"><p>CFI</p></td><td char="." align="char"><p>0.98</p></td><td char="." align="char"><p>0.98</p></td></tr><tr><td align="left"><p>TLI</p></td><td char="." align="char"><p>0.97</p></td><td char="." align="char"><p>0.96</p></td></tr><tr><td align="left"><p>NFI</p></td><td char="." align="char"><p>0.95</p></td><td char="." align="char"><p>0.94</p></td></tr><tr><td align="left"><p>RMSEA</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char"><p>0.04</p></td></tr></tbody></table> </ephtml> </p> <p>Graph: Fig. 2 Measurement model for Attitude to Business Analytics (ABA) questionnaire</p> <hd id="AN0152424141-14">Reliability and discriminant validity</hd> <p>Table 6 shows the internal consistency reliability (Cronbach's alpha coefficient) and the mean correlation of a scale with the other BSCLEI scales (an indicator of discriminant validity) for each BSCLEI scale. The range of scale reliabilities from 0.70 to 0.84 is considered adequate to good (Cronbach [<reflink idref="bib11" id="ref91">11</reflink>]). The range of mean correlations from 0.25 to 0.47 is smaller than the reliabilities and so indicates that scales measure distinct, but correlated, aspects of learning environment. Table 6 also shows the internal reliability and mean correlations for each ABA scale. The range of internal reliabilities from 0.88 to 0.93 is good to excellent and the range of mean correlations between the scales of 0.35 to 0.51 suggests that ABA scales measure distinct but correlated aspects of the student attitudes to business analytics.</p> <p>Table 6 Internal consistency reliability (alpha coefficient) and discriminant validity (mean correlation with other scales) for each BSCLEI and ABA scale</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Questionnaire</p></th><th align="left"><p>Scale</p></th><th align="left"><p>Alpha reliability</p></th><th align="left"><p>Mean correlation</p></th></tr></thead><tbody><tr><td align="left"><p>BSCLEI</p></td><td align="left"><p>Student Cohesiveness</p></td><td char="." align="char"><p>0.84</p></td><td char="." align="char"><p>0.25</p></td></tr><tr><td align="left" /><td align="left"><p>Integration</p></td><td char="." align="char"><p>0.73</p></td><td char="." align="char"><p>0.38</p></td></tr><tr><td align="left" /><td align="left"><p>Technology Adequacy</p></td><td char="." align="char"><p>0.70</p></td><td char="." align="char"><p>0.33</p></td></tr><tr><td align="left" /><td align="left"><p>Involvement</p></td><td char="." align="char"><p>0.76</p></td><td char="." align="char"><p>0.39</p></td></tr><tr><td align="left" /><td align="left"><p>Task Orientation</p></td><td char="." align="char"><p>0.81</p></td><td char="." align="char"><p>0.47</p></td></tr><tr><td align="left"><p>ABA</p></td><td align="left"><p>Anxiety</p></td><td char="." align="char"><p>0.93</p></td><td char="." align="char"><p>0.35</p></td></tr><tr><td align="left" /><td align="left"><p>Enjoyment</p></td><td char="." align="char"><p>0.91</p></td><td char="." align="char"><p>0.48</p></td></tr><tr><td align="left" /><td align="left"><p>Usefulness of Course</p></td><td char="." align="char"><p>0.91</p></td><td char="." align="char"><p>0.51</p></td></tr><tr><td align="left" /><td align="left"><p>Usefulness in Employability</p></td><td char="." align="char"><p>0.88</p></td><td char="." align="char"><p>0.50</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>N </emph>= 275</p> <hd id="AN0152424141-15">Predictive validity of BSCLEI: Correlations between learning environment and attitude scales</hd> <p>The correlation between each attitude scale (ABA) and each learning environment scale (BSCLEI) was used as an index of discriminant validity. Table 7 reports these correlations, which replicate considerable past research evidence (Fraser [<reflink idref="bib18" id="ref92">18</reflink>], [<reflink idref="bib19" id="ref93">19</reflink>]) for positive associations between student attitudes and learning environment and negative associations between student anxiety and learning environment. Specifically, Table 7 shows:</p> <p>Table 7 Predictive validity: Correlations between attitude and learning environment scales</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Environment scale</p></th><th align="left" colspan="4"><p>Correlation</p></th></tr><tr><th align="left" /><th align="left"><p>Anxiety</p></th><th align="left"><p>Enjoyment</p></th><th align="left"><p>Usefulness of course</p></th><th align="left"><p>Usefulness in employability</p></th></tr></thead><tbody><tr><td align="left"><p>Cohesiveness</p></td><td char="." align="char"><p>− 0.11</p></td><td char="." align="char"><p>0.13*</p></td><td char="." align="char"><p>0.04</p></td><td char="." align="char"><p>0.24***</p></td></tr><tr><td align="left"><p>Integration</p></td><td char="." align="char"><p>− 0.33***</p></td><td char="." align="char"><p>0.38***</p></td><td char="." align="char"><p>0.26***</p></td><td char="." align="char"><p>0.42***</p></td></tr><tr><td align="left"><p>Technology Adequacy</p></td><td char="." align="char"><p>− 0.30***</p></td><td char="." align="char"><p>0.30***</p></td><td char="." align="char"><p>0.19**</p></td><td char="." align="char"><p>0.33***</p></td></tr><tr><td align="left"><p>Involvement</p></td><td char="." align="char"><p>− 0.32**</p></td><td char="." align="char"><p>0.36***</p></td><td char="." align="char"><p>0.21***</p></td><td char="." align="char"><p>0.34***</p></td></tr><tr><td align="left"><p>Task Orientation</p></td><td char="." align="char"><p>− 0.42***</p></td><td char="." align="char"><p>0.40***</p></td><td char="." align="char"><p>0.20***</p></td><td char="." align="char"><p>0.35***</p></td></tr></tbody></table> </ephtml> </p> <p>*<emph>p </emph>< 0.05, **<emph>p </emph>< 0.01, ***<emph>p </emph>< 0.001 <emph>N </emph>= 275</p> <p></p> <ulist> <item> For three attitude scales (Anxiety, Enjoyment, and Perceived Usefulness in Employability), correlations with four learning environment scales (Integration, Technology Adequacy, Involvement, Task Orientation) were statistically significant and of moderate magnitude according to Cohen's ([<reflink idref="bib10" id="ref94">10</reflink>]) criterion that moderate correlations range between 0.3 and 0.5</item> <p></p> <item> For one attitude scale (Usefulness of Course), the correlations with four learning environment scales (Integration, Technology Adequacy, Involvement, Task Orientation) were statistically significant and of small magnitude according to Cohen's ([<reflink idref="bib10" id="ref95">10</reflink>]) criterion that small correlations are less than 0.3.</item> <p></p> <item> For the learning environment scale Cohesiveness, the correlation was statistically significant with small magnitude for the attitude scales of Enjoyment and Perceived Usefulness in Employability.</item> </ulist> <hd id="AN0152424141-16">Discussion and conclusion</hd> <p>This learning environment study is distinctive in that it focused on the neglected higher-education level, in contrast to voluminous past research at the school level (Alansari and Rubie-Davies [<reflink idref="bib2" id="ref96">2</reflink>]), and on the university subject of statistics that is notoriously difficult to teach and learn.</p> <p>Our research involved validating two existing instruments—the BSCLEI to measure aspects of the learning environment of computer-based statistics workshops and the ABA to measure students' attitudes toward a business statistics course—with 250 students in a UK university undertaking a whole-year course involving a large lecture (in excess of 250 students) every two weeks and practical workshops of no more than 20 students every week. The workshops were run by academic staff (not teaching assistants) and most workshops were conducted by instructors different from the ones who delivered the lectures. Because the course assessment consisted of one final written examination, the exercises that the students undertook in the workshops did not count directly towards final grades, but the knowledge and skills covered in workshops were relevant to the final examination.</p> <p>Principal component analysis and confirmatory factor analysis supported the factor structure of both the BSCLEI and the ABA in a setting different from previous uses of these instruments (Nguyen, Newby & Skordi [<reflink idref="bib50" id="ref97">50</reflink>]), with all items loading on their a priori scales. The BSCLEI has five factors that explained 63.40% of the variance and whose reliabilities that ranged from 0.70 (classified as acceptable) to 0.84 (classified as good). The ABA has four factors that explained 75.89% of the variance scale reliabilities that ranged from 0.88 (good) to 0.93 (excellent).</p> <p>Consistent with past studies, ABA scales showed high mean correlations (discriminant validity). BSCLEI scales had higher mean correlations than in a previous study, perhaps because the setting of the current study is different from the previous one. Because the current research was carried out in the UK, in contrast to previous studies in the USA (Nguyen, Newby and Skordi [<reflink idref="bib50" id="ref98">50</reflink>]), subtle differences between American English and British English might have led students to interpret items slightly differently. Also, the structure of the classes was somewhat different in the current study that involved unambiguously computer laboratory classes in which students were given specific sets of exercises to complete during class time, with the theory relevant to the exercises being covered in separate lectures. In previous studies, classes were run in computer laboratories but were a mixture of theory and practice with exercises given to students on an ad hoc basis (Nguyen, Charity and Robson [<reflink idref="bib49" id="ref99">49</reflink>]).</p> <p>Correlation analysis (predictive validity) revealed moderate relationships between four learning environment variables (Integration, Technology Adequacy, Involvement and Task Orientation) and three attitude variables (Anxiety, Enjoyment and Perceived Employability), but Usefulness of the Course correlated only weakly with these environment variables. Because the classroom environment is influenced by both the design of a course and the instructor's approach, correlations were anticipated between environment and attitude variables (Fraser [<reflink idref="bib18" id="ref100">18</reflink>]). This supports previous findings (Meletiou-Mavrotheris, Lee and Fouladi [<reflink idref="bib44" id="ref101">44</reflink>]) that courses with a clear link between theory and practice, hardware and software that work without problems, student participation during workshops and clear student expectations are associated with less anxiety, greater student enjoyment, and positive views of the usefulness of the course and of statistics in general. The weak and nonsignificant correlation between anxiety and Student Cohesiveness is consistent with previous studies involving laboratory classes (Nguyen et al. [<reflink idref="bib50" id="ref102">50</reflink>]; Newby and Fisher [<reflink idref="bib47" id="ref103">47</reflink>]).</p> <p>The relatively strong associations between student attitude variables and Integration and Task Orientation suggest that, for courses structured with large lectures and smaller workshop sessions, course coordinators should ensure that workshop exercises are clear and relevant to the topics covered in the lecture. Correlations between student attitudes and both Technology Adequacy and Involvement suggest the importance not only of having appropriate hardware and software, but also for students actually to complete the workshop exercises.</p> <p>The correlation analysis shows that the learning environment of a business analytics workshop is associated with students' attitudes in terms of anxiety, enjoyment, and perceived usefulness of the course and of business analytics in general. For a course structure that involves a large lecture plus a number of small practical workshops, the most salient learning environment variables appear to be Task Orientation and Integration.</p> <p>As anticipated, there are limitations to our study. Because the sample from a UK university comprised first-year undergraduate business students, the majority of students were under the age of 20 years and so had little experience of a business environment. Therefore, in future research, it could be revealing to administer these instruments to graduate students, who are more likely to have real business experience, especially because there seem to be major problems in teaching business analytics at the graduate level (Nguyen et al. [<reflink idref="bib49" id="ref104">49</reflink>]). Also, because the BSCLEI's scales were chosen by the researchers because they are likely to be associated with student attitudes, it would be valuable in future research to expand the number of scales to include other aspects of the learning environment. Despite these limitations, this research confirmed the validity of these instruments in a new setting, thus suggesting that they can be used with confidence in future research and practical applications.</p> <hd id="AN0152424141-17">Acknowledgement</hd> <p>We are grateful to Michael Newby for his insightful comments on a draft version of this article.</p> <hd id="AN0152424141-18">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0152424141-19"> <title> References </title> <blist> <bibl id="bib1" idref="ref41" type="bt">1</bibl> <bibtext> Afari E, Aldridge JM, Fraser BJ, Khine MS. 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  Data: Computer Laboratory Workshops as Learning Environments for University Business Statistics: Validation of Questionnaires
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  Data: <searchLink fieldCode="AR" term="%22Nguyen-Newby%2C+Thuyuyen+H%2E%22">Nguyen-Newby, Thuyuyen H.</searchLink><br /><searchLink fieldCode="AR" term="%22Fraser%2C+Barry+J%2E%22">Fraser, Barry J.</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-1026-9495">0000-0003-1026-9495</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Learning+Environments+Research%22"><i>Learning Environments Research</i></searchLink>. Oct 2021 24(3):389-407.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 19
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Computer+Centers%22">Computer Centers</searchLink><br /><searchLink fieldCode="DE" term="%22Business+Administration+Education%22">Business Administration Education</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics+Education%22">Statistics Education</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Environment%22">Educational Environment</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Validity%22">Test Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Workshops%22">Workshops</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22United+Kingdom%22">United Kingdom</searchLink>
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  Data: 10.1007/s10984-020-09324-z
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  Data: 1387-1579
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  Data: Research on learning environments at the higher-education level has been quite sparse compared with studies at other educational levels. Because statistics is perceived as a difficult subject across disciplines, it suffers from low passing rates in many universities. This study involved validating questionnaires for assessing the psychosocial environment and student attitudes associated with learning business statistics in computing laboratory workshops. The Business Statistics Computer Learning Environment Inventory (BSCLEI) and Attitude to Business Analytics instrument were validated with 275 students enrolled across various business degree programs in the United Kingdom over two academic years. Various data analyses (including exploratory and confirmatory factor analyses) supported the validity of these two questionnaires, thereby paving the way for their future use in research and practical applications relevant to learning environments in higher-education statistics workshop classrooms.
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        StartPage: 389
    Subjects:
      – SubjectFull: Computer Centers
        Type: general
      – SubjectFull: Business Administration Education
        Type: general
      – SubjectFull: Statistics Education
        Type: general
      – SubjectFull: Educational Environment
        Type: general
      – SubjectFull: Questionnaires
        Type: general
      – SubjectFull: Test Validity
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Workshops
        Type: general
      – SubjectFull: United Kingdom
        Type: general
    Titles:
      – TitleFull: Computer Laboratory Workshops as Learning Environments for University Business Statistics: Validation of Questionnaires
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Nguyen-Newby, Thuyuyen H.
      – PersonEntity:
          Name:
            NameFull: Fraser, Barry J.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 1387-1579
          Numbering:
            – Type: volume
              Value: 24
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Learning Environments Research
              Type: main
ResultId 1