Learning Community and Its Impact on Attitude toward Computer-Based Statistics

This study examined the three dimensions that should exist in a learning community, namely Student Cohesiveness, Integration, and Task Orientation, related to their influence on attitude toward computer-based statistics. Attitude toward computer-based statistics itself is measured using constructs of the revised Technology Ac-ceptance Model (TAM). This study was designed to justify the value of information systems (IS) in overcoming accounting students’ statistical problems. The use of IS probable to reduce the pressure in dealing with statistics so that there is an opportunity to increase accounting students’ competitive advantage. The respondents consisted of 105 undergraduate accounting students. The data was collected using a 5-scale Likert questionnaire then analyzed using Structural Equational Modelling (SEM). With purposive sampling, this study was collected 105 responses obtained from private and state universities. The results indicate that task orientation is the key indicator of the learning community, affecting attitude toward computer-based statistics. Meanwhile, the second-order factors show that all three predictors were essential in explaining attitude toward computer-based statistics and significantly impacted Reuse Intention. This study also suggests implementing an informal learning community to build learning dynamics that are more independent but still controllable so that the learning topic is integrated with certain subjects

velopments have provided opportunities to bridge the expectation gap.
The presence of complex and comprehensive data analysis software makes students easy to analyze complex and advance statistical technique effectively and efficiently. It is, indeed, the result of the development of Information Systems and Technology (IST) that useful to reduce the sacrifice and workload of its users (Sagala et al., 2017). Similarly, in the use of statistical software, IST can reduce the workload when analyzing data and transform anxiety and stress in data analyzing into an easy and enjoyable process (Hafsah et al., 2018;Sagala et al., 2017). In real business practices, this way of working will spur performance, productivity, and comfort of work on the professional business side that comes from himself. Wardoyo (2016) said that the fast development of the information technology has caused various changes on the way how people live including education. Educational updates are needed and become a guide to improve the quality of education (Putri, 2017). Universities need to adjust for changes, one of them take the role of maximizing the learning output of quantitative subjects. Higher education are responsible for facilitating students in developing their intellectual abilities to meet their profession's expectations so that the student ready to compete in the actual work and business (Fogarty et al., 2016).
Programs that organize economic, accounting, and business studies should provide students with a better and more streamlined learning experience. Learning involving quantitative analysis should have integrated IST as a tool to facilitate data analysis. Although students still have to understand the manual logic, urgency, essence, and rule of thumb of the statistical tools, in practice, the use of statistical software will significantly assist student performance in translating phenomena (interpretations), capturing conclusions, and making decisions.
On the learning side of tertiary institutions, some researchers offer a shift in learning styles in tertiary institutions towards a learning process that can develop an understanding of accounting and business concepts and principles rather than merely technical aspects (Flood & Wilson, 2008). This view seems to be in line with statistical learning in the fields of accounting and business. Students need broader concepts of practice and the implications of data analysis. It certainly creates new challenges because of the limited time allocation during the lecture.

INTRODUCTION
The practice of business professionals is becoming more dynamic and competitive nowadays. The situation requires business professionals to have reliable analytical power that useful for analyzing business profitability and its precedence. The analysis method of some research, in general, is divided into qualitative and quantitative. Qualitative analysis generally refers to various factors that emerge from the business operation's nature and expertise developed based on experience. Meanwhile, quantitative analysis is more measurable and its expertise is taught in universities in various subjects, such as economics mathematics, business statistics, cost accounting, investment analysis, financial management, and inferential statistics. In line with this argument, previous research revealed that such quantitative expertise really needs to be developed and indeed it should be integrated into accounting curricula around the world (Nguyen et al., 2016;Warwick & Howard, 2016). The urgency is due to the quantifiability of quantitative data makes clear business decisions making indicators.
The need for quantitative expertise in business and accounting professionals has also been conveyed by business school's accreditation associations such as the Association to Advance Collegiate Schools of Business (AACSB), the Association of MBAs (AMBA), and the Chartered Institute of Personnel and Development (CIPD) (Nguyen et al., 2016). The associations said that business masters (MBA/M.Acc) must possess quantitative data analysis expertise and interpretation to capture measurable trends, opportunities, cost behavior, and market phenomena to produce the right business decisions (See: Nguyen et al., 2016). Unfortunately, accounting and business students often have concerns when interacting with advanced statistical calculations to conduct quantitative data analysis (Onwuegbuzie, 2000;Warwick & Howard, 2016;Williams et al., 2008).
Accounting students' constraints in analyzing statistical data are not new and specific problems that only occur in Indonesia but are common problems arising in various parts of the world (see: Onwuegbuzie, 2000;Warwick & Howard, 2016;Williams et al., 2008). This condition results a gap between business school accreditation associations expectation and student competency as prospective accountants and business professionals. Accounting student will face competitive business which challenge their professionalism as individuals. However, technological de-offered to learn communities as informal learning that supports formal learning activities in the classroom. The learning community is becoming essential due to the limited time allocation at the school. Simultaneously, many practical aspects and implications that need to be further discussed are related to developing student competencies, for example, in the use of data analysis software in conducting quantitative studies. To support the classroom's learning process, students need to strengthen knowledge outside the classroom through directed discussion with their colleagues to complete assignments, projects, mini research, and so on (Hafsah et al., 2018).
Universitas Negeri Medan (Unimed), through the chancellor's regulations, actually accommodated the assignment, such as routine tasks (RT), journal review (JR), mini-research (MR), project (PJ), critical book review (CBR), and idea engineering (IE). However, learning outside the classroom will be effective with a learning community that is a place for students to exchange ideas and discuss. The concept of this community has been widely developed in knowledge management (Dalkir, 2013). Research of Hafsah et al., (2018) and Hasibuan et al., (2020) have also revealed that the learning community has a significant impact on academic performance and student capabilities mastering computerbased statistics.
It needs to be explored further regarding what the learning environment should be accommodated in these learning activities. So, the learning community can be instrumental in improving academic performance and expertise in specific fields. Therefore, this study seeks to investigate the dynamics of learning in student learning communities in the Faculty of Economics, which are associated with attitudes toward computer-based statistics and intentions to reuse them. This study attempts to answer the following questions: RQ1: How can community of learning affect attitude toward computerbased statistics among accounting students? RQ2: Does the student's attitude toward computer-based statistics affect the student's re-use intention?
Several studies reveal that activities such as learning communities should be carried out with some control carried out by lecturers or facilitated by the campus even though learning runs informally (Cavanagh et al., 1998;Hafsah et al., 2018;Hasibuan et al., 2020;Nguyen et al., 2016). Thus, in the community, students can learn textual kno-wledge and the implications of their knowledge and dynamics and culture of learning environments that become miniatures of real work-life (Fullan, 1993;Nguyen et al., 2016;Sergiovanni, 1993).
The learning community dynamics will be examined for their impact on attitude toward computer-based statistics. Computer-based statistics is a data analysis tool that is operationalized by the IST platform. Attitude toward computerbased statistics refers to the Technology Acceptance Model (TAM) construct developed by Davis, (1989) and Davis et al., (1989) and extended by Venkatesh & Davis, (2000).
The model measures the user's attitude in accepting and using an IST; in this case, the statistics software (Davis, 1989;Davis et al., 1989). These dimensions are 1) perceived ease of use, which related to the less effort to operate new applications, for example, the easiness of student to operating statistical software; 2) perceived usefulness, which related to the sensation of the usefulness of applications used to help complete productive tasks, for example, statistical software that helps statistical data analysis tasks; and 3) perceived enjoyment, which related to the design of user interface that can produce convenience in using an IST, such as statistical applications that can be enjoyed and makes data analysis work fun (Davis, 1989;Davis et al., 1989;Venkatesh & Davis, 2000). Davis (1989) and Davis et al. (1989) developed TAM based on the Theory of Reason Action (TRA) (Fishbein & Ajzen, 1977 and the Theory of Planned Behavior (TPB) Ajzen & others, 1991Ajzen & others, )1985Ajzen & others, , 1987, which explains that individual actions are born from rational and planned reasons. In connection with IST's use to analyze data, previous research has revealed that adopting IST in certain jobs can reduce workloads (Effiyanti & Sagala, 2018). The pressure from workloads possible to transformed into comfort by utilizing IST (Sagala et al., 2017).
Other studies also reveal that user attitudes towards IST can affect job satisfaction and impact sustainability using IST (Grimm & Blazovich, 2016;Sagala et al., 2017;Sagala & Sumiyana, 2020;Wang & Scheepers, 2012;Wu & Lu, 2013). In this research, the learning community is thought to influence attitude toward computerbased statistics, which will make students take the initiative to adopt data analysis software to help complete their assignments. Furthermore, the acceptance of the statistical software will encourage them to use the application again when he is faced with similar tasks in their professional career in the future.
When faced with quantitative data analysis tasks in their job or business, they will easily face it by maximizing the use of statistical data analysis software. This will certainly increase the competitive advantage for individuals professionally. This study aims to examines the three dimensions that should exist in a learning community, namely Student Cohesiveness, Integration, and Task Orientation, related to their influence on attitude toward computer-based statistics

METHODS
This study's subjects were all students at the Faculty of Economics, Medan State University, who were members of the learning community within the Unimed Faculty of Economics. We used the purposive sampling technique to collect the data. It used to ensure that the collected data is relevant to explaining the student community environment (Sekaran, 2010). The unit of analysis in this research is the individual so that the representation of the results will refer to individual perceptions.
Data for all variables in this study were collected using a questionnaire with survey methods. The survey is a measurement process used to gather information in a well-structured interview, with or without the interviewer (Cooper et al., 2006). We used an electronic questionnaire and distributed it with snowball techniques. The technique was chosen to ensure random sampling and guarantee the independence of respondents to avoid response bias. Respondents were voluntarily deciding whether or not to become respondents.
The instrument in this study was designed with a 5-Likert scale. The instrument was adapted from the research of Wang & Scheepers (2012), Nguyen et al. (2016), and Venkatesh & Davis (2000). The instrument adapted from Nguyen et al. (2016) is for student cohesiveness, learning integration, and task orientation variables. Wang and Scheepers (2012) for the intention to reuse variables and perceived comfort. The instrument's perceived usefulness, ease of use, and enjoyment were adapted from Venkatesh and Davis (2000).
The researchers have then tested the construct validity of the collected data. Construct validity is carried out in three stages, namely convergent validity, discriminant validity, and reliability. After obtaining a valid construct, a Structural Equational Modeling (SEM) test was performed to test the research model. Data analysis was performed using software named SmartPLS 3.0.

RESULTS AND DISCUSSION
This study has 105 collected data. The data is then tabulated and analyzed. The data tabulation on the sample demographics shows that of the 105 respondents, there were 25 (23.82%) male respondents and 80 (76.19%) female respondents. This figure shows that the respondents are predominantly female. This cannot be controlled because women dominate the demographics of students at the Faculty of Economics. Furthermore, respondents are represented from the age range of 19 to 23 years from the age aspect. This is good because respondents are represented from each class. The response obtained can indicate the general response of the Faculty of Economics students regarding their involvement with the learning community.

Construct Validity
Furthermore, the researchers tested the construct validity by cross-loading to measure convergent validity, Root of AVE and correlation matrix to measure discriminant validity, and Cronbach's Alpha to measure reliability (Hair et al., 2009). The results of cross-loading measurements are presented in Table 2. The cross-loading results show that each loading has a number> 0.7, and no loading has a number above> 0.7 in more than one construct (Hair et al., 2009). Thus, no measurement items were dropped, and each dimension met convergent validity.
Convergent validity in this study was carried out by reviewing the factor loading and average variance extracted (AVE) values. Factor loading indicates that all latent constructs must be higher than 0.5 (Fornell & Larcker, 1981;Hair et al., 2009). The results showed that the overall fac-tor loading value was more than 0.50, both in the constructs of Integration, Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, Reuse Intention, Student Cohesiveness, and Task Orientation. Besides, this study measures AVE for each construct analyzed. This is to improve statistical conclusions from the results of convergent validity. Hair et al. (2009) argued that if the AVE value was more than 0.50, each construct had good convergent validity. Table 3 shows the AVE results, and it can be seen that each construct has met the convergent validity criteria because the AVE value is more than 0.5. After obtaining convergent validity, this study tested the discriminant validity, which showed that each construct was  (2020) completely different from other constructs (Hair et al., 2009). This study does this by observing AVE's root value by entering it diagonally into the correlation matrix and observing whether the AVE number is greater than the correlation value between variables at the bottom position (Hair et al., 2009). Table 3 shows the root AVE values, which are all higher than the correlation coefficient on that side. This condition indicates that each construct has met the criteria for discriminant validity. In the final section of construct validity, this study examines the data's reliability using Cronbach's alpha and composite reliability. Consistency of internal data reliability requires at least 0.6 for Cronbach's alpha value and composite reliability (Hair et al., 2009).
The lowest Cronbach's alpha value for each variable in this study was more than 0.80, as shown in Table 3. These results are consistent with the results of convergent and discriminant validity that have been tested previously. In the final section of construct validity, this study examines the data's reliability using Cronbach's alpha and composite reliability. Consistency of internal data reliability requires at least 0.6 for Cronbach's alpha value and composite reliability (Hair et al., 2009). The lowest Cronbach's alpha value for each variable in this study was more than 0.80, as shown in Table 3. These results are consistent with the results of convergent and discriminant validity that have been tested previously.

Second-Order Factor Analysis
The researchers carried out a second-order factor analysis to review the dimensions of the construct of Attitude Toward Computer-Based Statistics in the Faculty of Economics' learning community. Second-order factor analysis was performed to assess which dimensions were essential in establishing attitude toward computerbased statistics (Rindskopf & Rose, 1988). The factor analysis used was Confirmatory Factor Analysis (CFA) (Hair et al., 2009;Rindskopf & Rose, 1988). CFA was chosen because the constructs used had been built by previous researchers (Nguyen et al., 2016), while this study further confirmed its suitability with the situation at the Faculty of Economics, Universitas Negeri Medan.
The results of the second-order factor analysis can be seen in Figure 1. In the second-order factor analysis, each dimension's coefficient is treated as a loading factor (Rindskopf & Rose, 1988). Figure 1 shows that the dimensional coefficient is both perceived usefulness, perceived ease of use, and perceived enjoyment have a loading value of> 0.8. Thus, the three dimensions have good validity to form the construct of Attitude Toward Computer-Based Statistics. Furthermore, in terms of coefficient weights, the Perceived Ease of Use dimension has the highest loading number, followed by the dimensions of Perceived Usefulness and Perceived Enjoyment. This figure is not far away. However, this figure can still be given attention to what aspects become a priority in optimizing the use of Computer-Based Statistics.

Structural Model
The structural model testing was carried out using the SmartPLS 3.0 application. Structural model testing is conducted to determine the coefficient value of the causality relationship between constructs, namely 1) the effect of Student Cohesiveness, Integration, and Task Orientation on Attitude Toward Computer-Based Statistics; and 2) the influence of Attitude Toward Computer-Based Statistics on Reuse Intention. Student Cohesiveness did not significantly affect Attitude Toward Computer-Based Statistics with a coefficient value of -0.03 and a t-statistic value of 0.17. The t-statistic number is below the t-statistical significance indicator, namely > 1.96 (Hair et al., 2009).  Meanwhile, integration shows a significant positive effect on Attitude Toward Computer-Based Statistics at α = 10% with a P-value = 0.055 (<0.10). Task Orientation has a significant effect on Attitude Toward Computer-Based Statistics at α = 5% with a p-value of 0.001 (<0.05). This condition shows that the key to the student community's learning environment, which is an important antecedent in shaping the behavior of using computer-based statistics, is task orientation. Meanwhile, Integration has a subtle influence, and student cohesiveness does not show any contribution at all.
Attitude toward Computer-Based Statistics shows a significant positive effect on Reuse Intention at α = 5% with a p-value of 0.000 (<0.05). This finding is important because Attitude Toward Computer-Based Statistics impacts the use of computer-based statistics further. As stated in previous research (Hafsah et al., 2018;Sagala et al., 2017), statistical mastery is an important instrument for accounting and business students today. Computer-based statistics will also increase business professionals' productivity and efficiency, and accountants in working using statistical analysis. The capacity building for using Computer-Based Statistics will prepare students to become competitive business professionals and accountants, considering that knowledge capacity is an important issue in winning the market.
This findings complement the study of Hasibuan et al. (2020), Hafsah et al. (2018), and Sagala et al. (2017), who have explored the use of learning communities to improve student competence at the Faculty of Economics. This study also strengthens the research findings of Nguyen et al. (2016), Dorman &Fraser (2009), andWalberg (1991), which previously indicated that the learning environment is an essential instrument to provide students with learning experiences to achieve learning outcomes. Because learning is a consequence of a socially cultured learning environment, in this case, the learning community is used as a social environment with a conducive learning culture so that this culture can be transmitted to its members.
The learning community is considered important because it helps students construct their knowledge to fulfill the cognitive domain and provides complex learning experiences that will help them build affective and psychomotor domains. In this study, the dimensions of the learning environment were examined in three aspects. It was found that the aspects of task orientation and integration should be given special attention to the study program manager. Integration is the relationship between the material discussed in class and the learning community's discussion agenda (Nguyen et al., 2016). Then, task orientation is a learning activity in the community leading to completing assignments, projects, research from classroom learning, or follow-up learning in class (Nguyen et al., 2016).
Task orientation and integration refer to the direction of activities and assignments given to the learning community to align with the learning outcomes aimed at the study program (Nguyen et al., 2016). This alignment is important to ensure that the dynamics that occur in the  (2020) learning community are in line with the learning agenda in the classroom. Through the dynamics of learning that is in line with a classroom learning agenda, specifically, in this study, the community's learning environment can build student attitudes toward computer-based statistics used to analyze data in Statistics, Forecasting, and Econometrics courses.

Figure 2. Structural Model
In this study, student cohesiveness was found to have no significant effect on attitude toward computer-based statistics. This finding does look different from the study by Nguyen (2016), which proposes these three dimensions as important variables in the learning community. However, the empiric condition and characteristics of students in different tertiary institutions may show different research results. Student cohesiveness can actually be assumed to have existed and been the reason for establishing a learning community because the learning community was established voluntarily based on student initiatives like CoP in business entities (Dalkir, 2013).
However, because the objects tested in this study are specific issues in statistics, student cohesiveness does not show a direct effect. In contrast, task orientation and integration do show a significant effect because they relate to the content of the discussions discussed in the learning community. Furthermore, attitude toward computerbased statistics was found to have a positive and significant effect on reuse intention. This indicates that students will always use computer-based analysis tools in their future professional agenda related to statistical analysis.
This intention to sustainability shows that students have believed that the use of IST in statistical analysis will help them achieve optimal performance and simultaneously reduce their effort (Davis, 1989;Davis et al., 1989;Venkatesh & Davis, 2000). This certainly strengthens the argument that statistical learning in tertiary institutions must integrate the use of IST to facilitate the learning process, strengthen the learning orientation of conceptual understanding and not technical calculations, and expand the variety of statistical studies. These things can be done because of the reduced effort in studying the technical data analysis so that this effort can be transferred to variations in data analysis techniques. Thus, student mastery of a data analysis technique is increasingly comprehensive.

CONCLUSION
This study explores the dynamics of learning that occur in the learning community at the Faculty of Economics. Learning dynamics in terms of the learning environment's construct with three dimensions, namely 1) integration, 2) student cohesiveness, and 3) task orientation. These constructs are then associated with Attitude Toward Computer-Based Statistics built by IT acceptance dimensions, namely 1) Perceived Ease of Use; 2) Perceived Usefulness, and 3) Perceived Enjoyment. Then, Attitude Toward Computer-Based Statistics is tested for its effect on Reuse Intention.
This study uses the second-order factor analysis to test the validity of the dimensions of Attitude Toward Computer-Based Statistics. The data analysis results show that each dimension offered can represent the construct of Attitude Toward Computer-Based Statistics. Furthermore, this study examines each construct that forms the learning environment using SEM. Interestingly, not all dimensions of the learning environment show the influence on Attitude Toward Computer-Based Statistics. Only Task Orientation has a significant effect, while Integration has a moderate effect. However, Attitude Toward Computer-Based Statistics shows a significant effect on reuse intention.
In general, this study yielded several implications. First, study program managers must foster the direction of learning that occurs in the learning community. However, learning communities are generally born voluntary and are nonformal in nature. However, this study's findings indicate that this non-formal learning community has a great opportunity to optimize competency achievement. Second, coaching in the learning community can be done by controlling the learning community's direction, referring to the integration and task orientation, which is the finding of this study. Third, study program mana-gers need to coordinate with lecturers who teach courses to optimize the learning community's use by directing project assignments and problembased learning that can be solved together in the learning community.
Theoretically, this research contributes to enriching the repertoire of learning research in higher education by borrowing theories and constructs that develop in the business sector, namely TAM (Davis, 1989;Davis et al., 1989;Venkatesh & Davis, 2000), knowledge management (Dalkir, 2013;Nonaka & Takeuchi, 1995). This strategy needs to be done to enrich the variety of research and capture broader phenomena in learning and learning dynamics in Higher Education. On the other hand, this study also seeks to bridge the research gap between tertiary institutions and the business sector.
In practical terms, this study recommends universities to manage non-formal learning that has been running voluntarily. This management really needs to be done so that the student community's potential can be optimized to help achieve not only learning outcomes but also achieve individual competitive advantages that are increasingly needed today. Higher education, through the management of the study program, can facilitate infrastructure and sustainable coaching by paying attention to aspects of integration and task orientation in the learning community.