Modern learning environments are based on the application of information and communication technologies (ICT), as an instrument for transmitting knowledge. Nowadays, online tools in learning environments have increased due to the widespread use of mobile devices and the development of telecommunication services. Social changing expanded the use of online learning tools that have traditionally been linked to distance learning, such as videoconferences, support classes through chats or forums, online tests, remote laboratories, or work groups via the web. These tools are implemented, as consequence of the development of own technology by incorporating a set of tools that already exist on the market (such as Meeting Burner, Tiny Chat, Classroom for blended learning, etc.). This technology facilitates the so-called “mobile learning” (m-learning). M-learning is understood as an electronic learning and teaching supported model by mobile devices. The distinctive features of mobile devices, such as constant communication and real-time access, are what can enhance certain pedagogies, such as self-directed learning [1
], which are desirable for lifelong learning. The terminology of m-learning supposes the integration of mobile computing devices within teaching and learning, being the teaching professionals themselves who must analyze the characteristics and attributes for the successful m-learning [2
]. It is teaching and training methodology that uses state-of-the-art mobile devices (Smartphones, tablets, I-pad, and others) as a means of transmitting knowledge in educational settings. The definition provided in [2
] is the framework of m-learning in our teaching–learning process, where the tools applied to the virtual environment are accessible from mobile devices, both for students and teachers. Also consider that this definition implies that it is in fact a compliance with the three essential aspects of m-learning pointed out by [1
]: Mobility, availability, and portability of learning process. The origin of teaching through electronic devices dates from the 19th century [3
], thereby m-learning could be misinterpreted as a form of update e-learning but using new and more modern mobile devices. However, the current conception implies transformation in the methodologies to be applied, taking the attitudes linked to learning through mobile devices into account.
Mobile computing devices can provide educational opportunities [5
], improving the learning process due to better interaction [7
] and nowadays m-learning is practically identified with distance learning due to their mutual synergy [8
]. When distance learning is carried out within an official university program, a distance teaching institution is guaranteeing the integration of online learning tools in a virtual learning environment (VLE). Usually, a VLE is specifically made for the teaching objectives, according to the university’s methodology. The VLE must consider the personal characteristics of students in the program, adapting the best possible combination of online learning tools in the present context of m-learning. The design of VLE for mature students must take student’s learning difficulties into account, such as lower technological aptitudes linked to older age students, family obligations, and less availability due to the fact that they are generally part-time students. Aspects, features, and advantages of m-learning (see Figure 1
) support the utility of online tools for learning in the Era of Mobile.
Despite the variety of ICT for the generation of virtual learning environments (VLE) and their advantages, these new teaching tools also present problems. ICT causes rapid changes in the teaching way, such as the communication channel, the technological training, economic resources, etc. In addition, specific psychological aspects of the teacher and the learner must also be considered, such as community feeling versus isolation feeling. The feeling of belonging to a group or community diminishes the feeling of frustration or isolation due to the lack of physical contact with peers and teachers. To address this problem, the teacher should reduce the physical and psychological distance, supplying it with what is called transactional presence [9
] (by knowing that there is someone on the other side of the mobile device). This argument justifies the extended acceptation of video in m-learning, by providing the positive effect due to the visualization of the teacher’s image [10
]. The analysis of the first virtual communities has already highlighted the importance of knowing students for the adequate design of positive virtual environments [11
]. Recent research revealed the need to identify the requirements and difficulties of students in order to improve the learning environment [13
]. Several research papers have shown evidence that using ICT as teaching tools could increase students’ engagement [14
] and teacher’s role [15
], because these tools could improve self-directed learning [16
]. In this way, ICT tools have extended the meaning of lifelong learning and have provided new learning opportunities [18
]. Some authors have shown that the mere presence of technological tools is not enough, with the context that they are applied in and the way in which they are used being more important for learning [1
]. Educational technology must be combined with the use of instructional methods and work formats suitable for learning and teaching in technological environments [20
Within heutagogy, which can be understood as adult education (mature students), lifelong education is considered necessary for social sustainability and, as indicated by UNESCO [21
], that promotes equal opportunities within communities, especially for workers. The use of appropriate tools and approaches can provide positive results in continuous learning and make virtual learning just as effective as permanent m-learning [22
]. Studies related to the use of m-learning in educational institutions examine student’s attitude towards the use and implementation of m-learning techniques for the sustainability of learning, with special emphasis on the importance of m-learning design [23
A problem for a sustainable virtual education is the fact that teachers meet technologically native students, who naturally expect the application of ICT in all aspects of their lives, including learning. However, teachers doubt the effectiveness of the use of technologies and m-learning as the main teaching support [24
], due to a big generational gap. It has been shown that teacher training in the use of new methodologies based on ICT in higher education, such as the educational video, can promote sustainable educational practices [25
]. Teachers must promote positive attitudes in students [26
]. Studies related to the use of ICT-based learning, such as cloud learning, show that it is necessary to understand the student’s attitude towards the sustainable use of learning, since the student’s attitude plays a vital role in contributing to sustainable learning [27
]. In the analysis of the intention to use tools based on ICT, it was found that the perceived utility by the students affected their intention to use them [28
The use of m-learning can contribute to the sustainability of education, but its adoption will depend on a critical analysis of contextual factors, such as those linked to attitudes of students and the quality of websites.
To harness the educational potential of modern technology, it is necessary to create suitable environments and the strategic use of a range of tools that allow autonomous learning, so it is important to evaluate the tools in the design of online environments. Whether perceived utility of online tools causes a positive effect on students, one would expect a positive effect on the m-learning process. Accordingly, the objective of this work is to carry out a measurement of perceived utility of online tools by accounting college students. Additionally, student’s attitude and perceptions towards several components of the learning environment can be considered as relevant factors for successful online learning. Consequently, in this work, several personal attitudes have also been analyzed.
3. Materials and Methods
The objectives in this work were reached in accordance with the aforementioned project explained above. Thus, the measurement of perceived utility of online learning tools, the objectives, and the hypotheses were designed and implemented as explained below.
3.1. Objectives and Hypotheses
In accordance with the research objectives in the learning environment, which are the measurement of the perceived utility of online tools in an m-learning environment, as well as its explanatory variables, various online tools were applied in the m-learning environment. Implemented online tools for accounting learning consisted of short videos, virtual tutorials, short comics, online questionnaires, online exams, forums and chats, online working groups, and web conferences; which were accessible to all students enrolled in an accounting course within an official program at the EHEA. To measure the perception of the utility of the learning tools, a questionnaire was developed ad hoc. The questionnaire was made up of 5 variables. Each one was supported for a constructor composed of a pool of items. Each item related to an aspect of the variable to be measured.
The variables were: (1) The affinity for accounting (subject under study), (2) the affinity for virtual learning environments, (3) the self-perception of the general skills necessary for accounting, (4) the perception related to the role of the teacher in the virtual learning environment, and (5) the perception of the utility of online tools. Each student was asked to rate each item. To guarantee the validity of the questionnaire, as is common in educational research, the preliminary questionnaire was piloted [64
]. The reliability of the questionnaire was ensured by the Cronbach’s α (see Table 1
), by considering that the minimum value for social sciences must be greater than 0.7 [65
The questionnaire was carried out in three phases. The first phase was carried out during time 1 (T1), after the start of the course and having already tried the tools for more than three weeks. At that time, variables related to the affinity for accounting (ACC_I), the affinity for learning in virtual environments (VLE_I), and the self-perception of general skills (GS_I) were measured. At time 2 (T2), at half-term, the perception of the teacher’s role was measured. At time 3 (T3), at the final term, the utility attributed to the online tools was measured.
The questionnaire was carried out sequentially in three phases, so that 105 students (T1) initially participated. Seventy-five of those who participated in the first phase also completed the second phase (T2). Finally, 60 of the previous students completed the third phase (T3). Therefore, for the sequential study, the data processing included only the 60 students who participated in all phases.
A summary of the content of each of the items that make up each variable is shown in Table A1
According to the measurement and the research objective, the following alternative hypotheses were tested, expecting the following relationships among variables (see Figure 2
H1: Self-perception on generic skills by students is a positive explanatory variable of the affinity for accounting learning by linear regression.
H2: Affinity for virtual learning environments by students is a positive explanatory variable of the affinity for accounting learning by linear regression.
H3: Affinity for accounting learning by students is a positive explanatory variable of perceived utility of online learning tools by linear regression.
H4: Perception of teacher’s role by students is an explanatory variable of perceived utility of online learning tools by linear regression.
The sample was made up of students who used the online learning tools and completed the questionnaires (T1, T2, and T3). Some of the socio-demographic characteristics of the sample are shown in Table 2
. Almost 67% were women. Fifteen percent were part-time students and 48.3% of the students were between the age of 26 and 36 years old. It should be noted that only 6.7% were over 50 years old and only 8.3% were 25 years old or younger. Sixty percent had no family responsibilities, compared to 40% who claimed to have family responsibilities (spouse, children, parents, etc., in their care). The questionnaire finished early January 2020.
3.3. Variables and Measures
Students’ positioning was summarized using scoring indicators. Score analysis is a popular statistical method of processing data for causal inference [66
]. They are usually applied to summarize, in a single number, the students’ attitude towards specific issue [68
]. In accounting, scoring indicators have been used to measure students’ attitudes towards the perceptions about Accounting [29
] and accounting teachers’ attitudes [71
]. Furthermore, in the field of accounting, scoring indicators have been applied to measure attitudes towards virtual learning environments [72
This research assumed that a higher indicator of student’s attitudes towards Accounting should lead to a higher perception about utility of online tools. In this way, the explanatory variables, which were designed as scoring indicators, offer a score of the student positioning regarding the drivers of perceived utility. Each variable was elaborated by quotient between the sum of the scores of all the items (see Table 2
) and the maximum score that variable could take. Thus, the following variables, were made:
Self-perception of own generic skills: ;
Affinity to accounting learning: ;
Affinity to Virtual Learning Environment: ;
Perception on the Teacher’s role: .
Furthermore, the explained variable, which is the indicator of perceived utility related to online tools, was similarly made and continues with the following mathematical expression: .
The variables were made by using similar items from prior works:
The attitude towards Accounting was composed of 9 items, which were self-constructed from prior works [29
The attitude towards VLE was composed of 7 items self-constructed from prior studies on functionality and interaction [41
The self-perception on general skills of students was composed of 6 items. These items were taken from the specifications in the Official Educational Report of the University Degree in Tourism, regarding generic skills for accounting matter. These generic skills are like those defined in other official programs in Spain and European Union, according to desirable skills for accountant professionals. These items have been involved in prior works [42
The positive perception on the teacher’s role was composed of 4 items, according to prior works [42
The perceived utility of online tools was composed of 9 items, and these were distributed as follows: 1 item related to the short introductory course to basic accounting, 2 related to forums, 2 related to self-evaluations, 3 related to videos of accounting contents, and 1 related to accounting short comics. All the items ask about the contribution of the specific tool in different aspects of learning, considering prior literature [42
3.4. Statistical Hypotheses Fit
Linear regressions have been applied to measure the predictive capacity of online tools in blended learning environments [73
]. More specifically, multi-regression analysis has also been applied to analyses attitudes towards ICT in online courses, finding that the effectiveness of online courses can be explained by student’s attitude towards ICT factors [74
]. Also, statistical correlations by SPSS have been applied for analysis of perceptions [75
]. In the same way, linear regression has been a good statistical tool to identify predictor variables based on students’ perceptions related to students’ experience in their use of online tools, such as videos [10
]. In the field of attitude analysis towards learning contents with a higher numerical component, regression analysis has been shown as a useful tool [76
], and regression has also been applied to measure the effect of accounting undergraduates’ attitudes related to ethical commitment [77
]. In educational studies, regression analysis is used to explore the relationship between students’ behaviors and achievements in online settings [78
According to prior literature, the statistics analysis of this work was based on linear regression to determine whether variables at T1 and T2 could be explanatory variables of the tools’ utility, which is the explained variable (dependent variable) at the end of the period (T3). Likewise, the explanatory relationships among variables at T1 were also analyzed by linear regression. Previously, in order to select the involved variables in the linear regression models, a correlation analysis was carried out to detect what variables had shown statistical association.
Correlation analysis was used to establish the statistical association between the variables. Since the correlation analysis only implies the association between two variables, but not the statistical causality of one over the other, linear regressions were applied to establish the causality.
Therefore, for each linear regression, the dependent variable and the independent variables (explanatory variables) are shown. In addition, for each regression, the fit of the model calculated by R2 is shown, which is used to see the degree of intensity or effectiveness that the independent variables have in order to explain the dependent variable.
Furthermore, for each regression, ANOVA is used to show the statistic and its level of significance (sig.). If the significance is less than or equal to 0.05, the null hypothesis is rejected and the alternative hypothesis is accepted, which is the one proposed in social sciences as an objective to be demonstrated. Likewise, for each regression, the non-standardized coefficients and their level of significance are determined. When the significance level is less than or equal to 0.05, it is statistically considered that the variable or constant for that significance provides an explanation for the model. In this way, those coefficients with a significance level greater than 0.05 do not contribute at all to the explanation of the dependent variable and, consequently, this can be eliminated from the model.
The present work analyzed what variables are associated and whether the relationship between them was explanatory, taking the phases of the study into account. Thus, different regressions were related until concluding the third phase of the study with the best explanatory regression of the perceived utility of online tools. In this way, the initial hypothetical model that is based on the specialized literature takes the form of a set of statistically causal relationships that explains the variable, which is the objective of the analysis. That is, the perceived utility of the online tools and determinant attitudinal variables.
5. Discussion, Limitations, and Conclusions
Learning in the mobile age is thinking about virtual learning environments without any physical classroom, without schedules, and without limitations on age, geographic location, or financial resources. The so-called m-learning is seen as the potential scenario for the sustainable development of continuing education throughout life. Considering the heutagogy approach, the continuing education of mature students throughout life is a challenge and, at the same time, a need established as a social objective by countries and international organizations [21
]. It is in this teaching field where m-learning can perhaps offer more possibilities in order to implement the sustainability of continuous learning [22
]. No one doubts the facts that the characteristics of mobile devices [1
] represent a set of advantages to facilitate this continuous and sustainable learning, due to the educational opportunities of IT [5
]. One of the drawbacks, especially at an early age, is the technological dependence that it can generate in the student, to the detriment of the physical and mental development. It is for this reason that the mere sum of teaching tools accessible from mobile devices, which are hosted on a website, do not represent a profitable m-learning environment for the sustainability of continuous learning throughout life [1
Due to effect that the attitude of students may have on the sustainability process of m-learning [23
], some studies concluded the need to analyze the perceptions of students about online tools [28
] and the teacher’s role [25
]. In the present study, the analysis regarding the student’s perspective has been focused on the analysis of the utility perceived by students regarding online tools, as well as the effect that certain attitudes or the teacher’s role may have on the perceived utility of the tools.
Consequently, two main objectives were set for this investigation. The first was to measure the utility perceived by students in relation to online tools in the m-learning environment. The second was to establish the effect of students’ attitudinal variables on their perception about utility of online learning tools. Thereby, four hypotheses were raised regarding the effect of attitudinal variables on perceived utility.
In relation to the first objective of the research, related to the perceived usefulness of online tools, the descriptive data allow us to conclude that mature students involved in a Tourism program in the EHEA consider learning tools highly useful (0.67 of a maximum 1.00, see Table 3
). Within the attitude variables, they think they have a very high affinity for accounting (0.7622) and for learning in the virtual environment itself (0.7543). According to the results of the previous studies, the positive attitude towards accounting is associated with the understanding of numerical and logical processes [34
]. The results of our study are in accordance with this affirmation, since the students also attributed a high score to their generic abilities (0.7322). They also show good predispositions for learning in virtual environments (0.7543), despite being mature students with family responsibilities. Both affinities (accounting learning and VLE), are positioned in the first quartile. Considering only the two scoring indicators in the first quartile (ACC_I and VLE_I), it could be assumed that both variables play an important explanatory capacity of the utility of online tools.
Regarding previous scoring indicators, the first conclusion of this work is the suitability of the variables developed to measure the affinity for accounting, the affinity for online learning, the self-perception of generic skills, the role of the teacher, and the utility of learning tools in m-learning (see Table 1
Taking the suitability of the measures developed for the variables (score indicators) into account and according to previous works [29
], the perceived usefulness of the learning tools in this line could be explained by the student’s attitude towards accounting and the teacher’s role.
In this way, attending to the second objective, related to the explanatory relationships between the attitudinal variables (ACC_I, GS_I, and VLE_I), the teacher’s role perception and the perceived utility of online tools (TOOLS_I), our results corroborate a positive and explanatory linear association between the role of the teacher and the perceived utility of online tools, as well as a similar explanatory relationship between affinity for accounting and the perceived utility of tools. The joint effect of both variables can explain 33.8% of the utility (see Figure 3
and Table 11
) and so therefore, the acceptance of hypothesis 4.
Consequently, the main conclusion is that the teacher’s role as an instructor for autonomous online learning in a process of sustainable distance education must be considered to plan the tools for the m-learning. Likewise, the affinity towards the learning contents might also be considered in order to design environments based on m-learning, due to the fact that the student’s attitude towards contents is related to the perceived utility.
Other secondary results confirm that the students give a high score to the teacher’s ability to solve problems [49
], the teacher’s role as motivator of m-learning process [45
], and the teacher’s role as online tools manager [51
]. Likewise, the results confirm the importance of the teacher’s role for the transactional presence, which occurs when the student perceives that there is someone on the other side of the mobile device during the learning process [54
]. These secondary findings would imply the innovative technological training of teachers in the use of methodology of m-learning for a comfortable m-learning. Also, this training must be focused on customizable virtual environments so that, whether affinity for contents is low, the teacher can promote online tools previously tested as useful tools for the subject content, such as the visual tools for abstract or numerical contents [36
] that are common in Accounting. It was demonstrated that visual tools in accounting learning can mobilize thought and reflection [79
] and undergraduate accounting students find the use of visual tools offers them the necessary time, flexibility, and functionality in their reflections [80
Although a positive association between satisfaction with online accounting learning and affinity for learning in VLE was found as in prior papers [41
], the results of our research did not find a direct linear association between affinity for VLE and perceived utility of online tools. Affinity for learning in virtual environments plays an indirect explanatory relationship related to accounting affinity, being the self-perceptions on generic skills the moderating variable between the previous two. This finding indirectly corroborates that certain generic skills act as drivers in student’s attitude towards accounting learning, such as analytical and numerical skills [35
This research focused on the specific aspect of measuring the perceived utility of online tools in an m-learning environment and the effect of attitudinal and perceptions variables, making it difficult to compare the results with other research with different variables and method. However, this comparison can be used to show whether results point to the same general conclusions, which serves to define theoretical frameworks and variables. Detailed study of these variables for each environment might improve the sustainability of m-learning. The characteristics of each m-learning environment and the research objectives are linked to the practical applicability of the results to each learning environment. This partial comparability of results could be considered as a limitation of this work, although results offer valuable information for the design and improvement of m-learning environments.
Finally, considering the fit of the model (R2
around 34%), it must be concluded that although the accounting affinity and the perceived teacher’s role favors the perceived utility of online tools in virtual learning environments, these variables are not the sole determinants of the perceived utility of online tools. Therefore, a future extension of this study would lead to the analysis of other explanatory variables of utility. In this sense, we share the idea of [19
] that the success of m-learning may be due more to the method of use and context of use of technological tools rather than to the tools themselves. Furthermore, according to [39
], in this context, the teacher plays a priority role in self-directed learning, bearing in mind that self-directed learning is considered essential for the sustainability of learning throughout life. This leads us to consider for future research the analysis of perceptions and attitudes in m-learning from the teacher’s perspective (mobile teaching or mobile education). As the authors of [1
] indicated, teachers need to be trained to incorporate online learning tools based on mobile devices, but technology and curriculum should also be integrated in order to ensure success in m-learning. We join the idea of [15
] that indicates that, sometimes, the failure of new teaching technologies is because the teacher’s attitude has not been considered for the methodological design of the teaching process. Experimental studies indicate the convenience of teacher’s training in skills to teach [81
], in order to improve the teaching planning. This idea could be extended to accounting teaching in m-learning. In line with what is pointed out by [82
], students’ demotivating attitudes should also be considered, but from the teacher’s perspective. This approach would be useful to plan instruction, especially in subjects with a high numerical and abstract content.