ORIGINAL RESEARCH published: 19 December 2019 doi: 10.3389/fpsyg.2019.02795 Online vs. Classroom Learning: Examining Motivational and Self-Regulated Learning Strategies Among Vocational Education and Training Students Carla Quesada-Pallarès 1* , Angelina Sánchez-Martí 2 , Anna Ciraso-Calí 2,3 and Pilar Pineda-Herrero 2 1 Serra Húnter Fellow, Department of Applied Pedagogy, Universitat Autònoma de Barcelona, Bellaterra, Spain, 2 Department of Systematic and Social Pedagogy, Universitat Autònoma de Barcelona, Bellaterra, Spain, 3 Department of Methods of Research and Diagnosis in Education, Universitat de Barcelona, Barcelona, Spain Numerous studies have been conducted to explore students’ employment of motivational and self-regulated learning strategies (SRL). Research highlights the importance of having motivated students equipped with strategies that help them self- Edited by: regulate their learning, this being highly important when learning is acquired through Luke Kutszik Fryer, online learning programs. Nonetheless, such research has been scarce with Vocational The University of Hong Kong, Hong Kong Education and Training (VET) students; this is the gap in the literature this paper aims to Reviewed by: address. The article analyzes the degree to which VET students employ motivational and Sue Erica Smith, SRL strategies by comparing them according to the learning mode chosen. To achieve Charles Darwin University, Australia this, a quantitative approach was adopted to carry out a cross-sectional study. A total Evely Boruchovitch, University of Campinas, Brazil of 577 first-year VET students responded to an online questionnaire based on some Estrella Fernández, of the motivational and SRL strategies scale included in Pintrich’s model. Statistical University of Oviedo, Spain analyses were applied to test two hypotheses. Pintrich’s model was validated through *Correspondence: Carla Quesada-Pallarès a confirmatory factor analysis considering its application to Catalan VET students for carla.quesada@uab.cat the first time. The results reveal significant differences between classroom and online students in terms of levels of metacognitive self-regulation and effort regulation when Specialty section: This article was submitted to starting a VET program. However, this difference might not be entirely explained by Educational Psychology, the learning mode chosen. The findings of this study will provide VET researchers a section of the journal and practitioners with a greater understanding of their students’ characteristics when Frontiers in Psychology starting the program and the means to develop strategies that ensure their engagement Received: 03 June 2019 Accepted: 27 November 2019 throughout the course. Published: 19 December 2019 Keywords: Vocational Education and Training, motivation, self-regulated learning strategies, online learning, Citation: learning mode Quesada-Pallarès C, Sánchez-Martí A, Ciraso-Calí A and Pineda-Herrero P (2019) Online vs. Classroom Learning: Examining INTRODUCTION Motivational and Self-Regulated Learning Strategies Among Vocational There has been an international explosion of online learning and products in recent years, including Education and Training Students. the online delivery of Vocational Education and Training (VET) programs (Brennan et al., 2001). Front. Psychol. 10:2795. Online VET changes the relationship between educators and learners, interaction with the learning doi: 10.3389/fpsyg.2019.02795 content, and among learners themselves, and contributes to alleviating time and space barriers. Frontiers in Psychology | www.frontiersin.org 1 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET Students enrolled on an online VET course have the freedom Studies on the concept of task value have obtained various to acquire learning whenever and wherever they have the results: if the task is of little value to students, they will not engage opportunity to. This situation allows students to control how they in it (Eccles and Wigfield, 1995; Neuville, 2004); high perceptions learn, the pace of their learning, and how to balance their daily of task value positively correlate with course grades (Brackney tasks with the need to attend the course (Graham, 2006; Alkis and Karabenick, 1995; Linnenbrink and Pintrich, 2003; Liem and Taskaya, 2018). However, it is important to remember that in et al., 2008; Najafi et al., 2018); and task value is a significant order “to succeed in autonomous online learning environments, predictor of course completion (Pachlhofer and Vander Putten, it helps to be a highly motivated, self-regulated learner” (Artino 2014; Cho and Heron, 2015; Vanslambrouck et al., 2018). In a and Stephens, 2009; Gegenfurtner et al., 2019). longitudinal study, Lee (2015) found that task value in online This paper analyzes the degree to which students employ learning remained stable during an entire semester, showing that motivational and SRL strategies a few months after beginning a if students are helped to recognize the value of a task at the VET program. The aims of the study implemented to ascertain beginning of a course, they will stay engaged for the duration of this were to provide VET researchers and practitioners with it. However, in a study comparing blended learning with online a greater understanding of their students’ characteristics when learning, Alkis and Taskaya (2018) only found task value to be a starting the program and the means to develop strategies that significant predictor of academic success in the former. ensure their engagement throughout the course. In the context of VET, Radovan and Radovan (2015) found Motivational and self-regulated learning (SRL) strategies that task value increased among students when they were employed by VET students have been underexplored in research, provided with realistic job-related tasks that put the learning making it difficult to have a clear idea of what these might they had acquired into practice. Therefore, when provided comprise (Savoie-Zajc et al., 2010). Various researchers (Cardinal, with powerful and meaningful learning environments, students 2010; Cournoyer et al., 2015; Dubeau et al., 2017) have suggested perceive their learning tasks as being more valuable. Another that the low number of studies in this area could be related to the study carried out with VET students by Dubeau et al. (2017) reputation that VET programs have of attracting less motivated identified four patterns related to motivational and individual students. Therefore, one reason for conducting this research is characteristics (exceptional, talented, low-achieving and drop- that to the best of our knowledge no studies have previously out), perception of task value being significantly higher among analyzed differences in students’ motivational and SRL strategies exceptional students. with regard to the different types of VET learning available Pintrich and DeGroot (1990) found not only motivational (traditional or online). variables but also cognitive processes such as learning strategies Much research has been conducted in education studies with to be important in explaining students’ academic success, as regard to the role academic motivation plays in student success stated by Martínez and Galán (2000). Specifically, in online (e.g., Brackney and Karabenick, 1995; Credé and Phillips, 2011). environments, high levels of academic motivation and self- Results show that the two variables are highly correlated (Müller, regulation were found to be due to the autonomous nature 2008; Park and Choi, 2009; Wang et al., 2013; Alkis and Taskaya, of online learning compared to traditional classroom contexts 2018). Indeed, Chen and Jang (2010) found high attrition rates (Artino and Stephens, 2009). Despite its theoretical fuzziness, to be negatively correlated with motivation in online learning self-regulation is acknowledged as a multidimensional and environments. This can be linked to Rovai and Jordan’s (2004) process-oriented research construct coming from educational findings that online learning is not suitable for all students and psychology (Kaplan, 2008; Prinz, 2019). Zimmerman (2000) that students succeeding in an online learning environment must regarded self-regulation as thoughts, feelings, and actions that are be both highly motivated and able to regulate their own learning planned and modified to the fulfillment of personal goals. Hence, (Garrison, 2003; Artino and Stephens, 2009). there is “no one set of cognitive, metacognitive, motivational, In the field of education, various motivational theories are and behavioral strategies that constitutes the desirable mode of employed in relation to academic motivation (Deci and Ryan, engagement in every setting and task” (Kaplan, 2008, 483). 1985; Berndt and Miller, 1990; Pintrich, 1991; Zimmerman et al., Thus, the first hypothesis of this study is as follows. 1992; Meece and Holt, 1993), even if these do include common variables such as intrinsic motivation, extrinsic motivation, H1. VET students employ motivational and SRL learning and self-efficacy, among others. Clark (1998) suggested that strategies in differing degrees according to the learning mode in order to obtain high commitment in a task, task value chosen (classroom or online). H1a for task value; H1b for is the most important of the motivational factors, whereas metacognitive self-regulation strategies; and H1c for effort Aristeidou et al. (2017) and Jung and Lee (2018) found regulation strategies. task value to be associated with learning engagement and therefore learners’ learning performance in online learning To be considered “self-regulated,” students must be committed (Zhang and Liu, 2019). The concept of task value derives and efficiently control their own learning process (Zimmerman, from expectancy-value theory (Eccles and Wigfield, 1995, 2002), 2015). However, learners’ self-regulated learning is neither easy understood as the extent to which tasks meet individual needs nor automatic (Pintrich, 1999). This translates into at least in pursuing a goal. In other words, how far students perceive three important qualities: (1) self-observation (monitoring one’s the task they are doing is important and useful for their actions and thinking processes); (2) self-judgment (evaluating future plans or goals. one’s performance); and (3) self-reactions (one’s response to Frontiers in Psychology | www.frontiersin.org 2 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET performance outcomes) (Broadbent and Fuller-Tyszkiewicz, students best implement effort toward the accomplishment of 2018). Other scholars add that holding positive motivational academic tasks (Zeidner and Stoeger, 2019). In this sense, beliefs (positive attributions) regarding one’s capabilities is also effort regulation occupies a key role in SRL and is understood required for higher levels of self-efficacy (Boekaerts et al., 2000). as a learning strategy that entails self-managing motivation In respect of this, SRL is a constructive process that develops or persistence (Theus and Muldner, 2019). It is related to with opportunities for self-directed practice over time. It is based conscientiousness and academic self-efficacy. on past experiences and personal, behavioral, and environmental Self-regulation also involves the transfer of self-regulation factors (Pintrich, 2000; Zimmerman, 2015). processes (knowledge, skills, and attitudes) to different learning The phenomenon of self-regulation is complex and has been situations and contexts, including work and leisure (Boekaerts, theorized in different ways. Most theories agree in highlighting 1999, cited in Liveris and Cavanagh, 2012). In fact, through behavioral, motivational, and cognitive processes as constitutive cyclical phases that explain the interrelation of the metacognitive parts of SRL. First, behavior self-regulation includes students’ and motivational processes involved in SRL at the individual control of resources, such as effort regulation, help seeking, and level, students acquire what is known as self-regulatory time/study management (Pintrich, 2004). Second, self-regulation competency (Zimmerman, 2000). of motivation and affect entails controlling and revising Due to the proliferation of digital environments, students motivational beliefs, such as self-efficacy and goal orientation, now have more opportunities for interaction and practice, to meet the demands of a task. And finally, self-regulation of however, the design of digital learning contexts needs cognition encompasses the control of deep processing strategies careful attention to ensure that the self-learning process is that lead to better learning and performance. optimized (Ting and Chao, 2013). Likewise, there is a lack In addition, several models of SRL (Sitzmann and Ely, 2011) of evaluations measuring the impact of SRL on students in have recently been analyzed and compared by Panadero (2017). digital environments (Pérez-Álvarez et al., 2018). Moreover, All models agree that SRL is cyclical and composed of different the degree to which learners use SRL strategies may mediate phases and subprocesses. However, the labels and processes in the effects of dispositional characteristics and psychosocial each phase differ from one model to another. Of such models, contextual influences on academic performance in highly Pintrich’s (2000). SRL model (2000) had a highly significant autonomous instructional settings. This has been understudied, impact in the field and is widely known for its development however, and warrants further empirical investigation because of an instrument to measure SRL: the Motivated Strategies it could have important educational implications for instructors for Learning Questionnaire. The model classifies phases that (Artino and Stephens, 2009). other SRL models commonly share and areas for SRL (Lee In summary, various studies have pointed out that online et al., 2019), dividing SRL into the following four phases: (1) students need to employ motivational and SRL strategies more Forethought, planning and activation of prior knowledge of the extensively in order to succeed academically. This raises a task, the context, and the self in connection with the task; (2) question regarding what variables -learning mode among them- Monitoring processes; (3) Control and regulation of different explain differing degrees of motivational and SRL strategies parts of the task, the context, and the self; and (4) Reaction employed by VET students when starting a program. Thus, the and reflection on the task, the context, and the self – each also second hypothesis of our study is as follows. with four different areas for regulation: cognition, motivation and affect, behavior and context. The degree of student learning H2. The learning mode chosen by VET students is a key varies according to key self-regulatory processes. Pintrich (2004) variable when explaining the degree to which they employ stated that these SRL strategies were systematically directed motivational and SRL strategies when starting the program. toward the achievement of learning goals and divided them into As mentioned, the general aim of this paper is to analyze three groups: (1) cognitive, (2) metacognitive, and (3) resource the degree to which VET students employ motivational and management. Cognitive strategies such as selective attention, SRL strategies a few months after beginning the program. In decoding or structuring enable students to fuse new and existing order to achieve this, the aim was divided into two specific information (Richardson et al., 2012). Metacognitive strategies goals: (1) to validate the adaptation of three scales measuring refer to an awareness of learning procedures so as to be able to task value, effort regulation and metacognitive self-regulation; establish goals; thus, they are related to mentally representing and (2) to identify differences in students’ motivational and SRL learning goals, designing action plans, self-monitoring progress strategies depending on the learning mode they have enrolled for and evaluating goal achievement. Finally, resource management (classroom or online). The results are presented in line with the strategies require students to use social and their own resources aims outlined above. to persist when confronted with a task (Richardson et al., 2012). Examples of students’ regulatory strategies for controlling resources other than their cognition include managing their time, effort and study environment, as well as the use of peer, teacher, MATERIALS AND METHODS and other help-seeking learning strategies such as benefiting from a study group. Design and Procedure Within resource management strategies and SRL behavioral In order to respond to the research aims, a cross-sectional design capacities (Pintrich, 2004), regulatory processes focus on how was used. An on-line questionnaire was administered to a sample Frontiers in Psychology | www.frontiersin.org 3 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET of classroom and online VET students during their first academic factors, such as the availability of the program in online mode, year, in two different ways. working while studying or family responsibilities. For classroom VET students, the course coordinators or group Once the VET programs had been selected, we proceeded to tutors–henceforth tutors–were in charge of administration, select 39 public schools that ran these programs: these comprised which took place in the classroom, in a group setting, on a day four schools per program–one in each of the four Catalan and at a time agreed with the research team. Tutors provided their provinces–except for one of the programs, which was only offered students with the link to the online questionnaire in class or via in two schools (leaving a total of 38 schools) plus one online Moodle; students could either access the tool using their mobile school offering all the selected VET programs. Private schools phone or administration took place in the IT classroom if they were excluded from the sample because of the diversity of their were not allowed to bring their phones to school. Tutors were VET teaching models, especially the online version. also responsible for reminding students that the questionnaire The final sample was composed of 577 first-year VET was completely anonymous and checking the last screen of the students, out of a population of 92,125 pupils enrolled on VET questionnaire to ensure the tool had been answered in full. programs in public schools in Catalonia (8,764 online students, For online VET students, the school directors uploaded 83,361 classroom VET students for the 2017–2018 school year), the links to the online questionnaire to the virtual learning according to the latest public data available from the Catalan environment–Moodle–and gave the students 2 weeks to respond Education Department (Departament D’educació [DE], 2018). to it. After a week, they sent a gentle reminder to all students in Table 1 shows the main characteristics of the sample (valid cases). order to obtain more responses. In both cases (classroom VET and online VET students) the Tools scales were applied in the same order and through the same The research tool comprised an ad hoc questionnaire in Catalan online platform. Response time varied largely, depending on based on various validated scales. The decision to translate the question routes in the part regarding demographic information; questionnaire into Catalan was based on the language policy of but typically, it took 15 min to answer the entire questionnaire. Catalonia: as an autonomous region of Spain, Catalonia has some The Research Ethics Committee CER (FCES-2018-04) educational provisions particular to its region, including using belonging to the International University of Catalonia (UIC Catalan as the language of instruction. Barcelona) approved the research design and implementation, The questionnaire included a first part with questions asking including all consent procedures followed in the study. All for demographic information, mainly multiple-choice items (age, participants were at least 16 years old and informed that they gender, school pathways, work experience), and a question about could refuse participation in the research or withdraw at any the main reason for enrolling on the VET program. The latter was moment. The questionnaire was anonymous and participants’ a multiple-choice item with responses adapted from the Spanish informed consent was implied through survey completion. version of the Vermunt Inventory of Learning Styles (Martínez- Fernández and Vermunt, 2013), specifically from the following Participants learning orientations: personally oriented, certificate-oriented, A purposive sampling technique was used to select potential self-test oriented, and vocation-oriented. participants. First, 10 VET programs were selected according to The second part of the instrument was composed of questions different criteria: the research team sought to include programs on cross-disciplinary skills (critical thinking, teamwork and from both of the levels offered within the initial Spanish VET communication), as well as the three variables related to system (ISCED 3B and 5); all included programs had to be motivational and SRL strategies: task value, effort regulation and offered in both classroom and online modes and had to match metacognitive self-regulation. Lastly, students had to evaluate the priority economic sectors, which were identified by experts their accomplishment of five key technical/professional skills in a previous phase of the research. It is important to clarify that specifically related to their VET program. although students were completely free to decide which learning All three scales related to motivational and SRL strategies -task mode they wanted to follow, this decision depended on many value, effort regulation and metacognitive self-regulation- were TABLE 1 | Description of the sample. Variable Distribution Gender 42.5% females; 56.2% males; 1.4% other or do not want to answer. Age Mean 24.89 years (20.65 years for classroom VET; 37.96 for online VET); standard deviation 9.756 years (4.71 for classroom VET; 9.67 for online VET). Type of program 75.4% classroom VET; 24.6% online VET. Program 56.3% technology sector; 43.2% health and care sector; 0.5% other sectors. ISCED level 42.3% level 3B; 57.2% level 5. Prior work experience 29.8% no; 70.2% yes (among whom, 39.3% had work experience related to the VET program they were attending). Main reason to enroll on VET program 28.8% personal interest; 25% to find a job in this sector; 24.3% to progress in my professional career; 9.7% to get a certificate; 7.5% other reasons; 4.9% to demonstrate to myself that I have the ability. Frontiers in Psychology | www.frontiersin.org 4 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET measured using our own adaptation of the corresponding factors Metacognitive self-regulation refers to exercising control over taken from the Motivated Strategies for Learning Questionnaire cognition and learning; it includes three general processes: (MSLQ) (Pintrich, 1991). A selection of only some of the factors planning, monitoring, and regulating. was required, since the questionnaire is very long and complex; furthermore, the MSLQ is modular, and it is very common Data Analysis for researchers to only use parts of it rather than the entire Since the original Pintrich (1991) scales had already been instrument (Holland et al., 2018). In order to choose the most translated into Catalan and adapted to fit the VET context, relevant scales, we considered metacognitive self-regulation and their validation was required. In order to obtain evidence of effort regulation to be the strategies most related to the concept of validity based on the internal structure, a confirmatory factor “learning to learn,” this emerging as one of the key competences analysis (CFA) was performed using the AMOS v.23 software. for apprentices in a previous phase of the research based on The original structure (Pintrich and DeGroot, 1990) was tested, interviews with stakeholders. In addition, task value was also with three correlated factors. After data depuration and inverting included as it is one of the key motivational factors that can reversed items, a maximum likelihood estimation method was impact learning engagement (Aristeidou et al., 2017; Jung and used (with regression imputation for missing values). Some Lee, 2018; Zhang and Liu, 2019), and therefore course completion readjustments were made to the final model by observing the (Pachlhofer and Vander Putten, 2014; Cho and Heron, 2015; regression weights and modification indexes. Vanslambrouck et al., 2018). Since online courses generally have Normality assumption of the factor scores was checked higher failed retention rates than classroom settings (Herbert, using the Kolmogorov-Smirnov test of normality, skewness and 2006; Smith, 2010; Bawa, 2016), the examination of this variable kurtosis intervals, visual inspection of normal and detrended could be especially relevant for online VET students, who would Q-Q plots. The Kolmogorov-Smirnov tests showed none of probably need to employ a high degree of task value in order to the three variables to be normally distributed (p < 0.0001 for successfully complete their studies. task value and effort regulation; p = 0.011 for metacognitive To ensure the transferability of results, a backward translation self-regulation). Task value and effort regulation distributions procedure was followed. Two native Catalan speakers who are revealed negative asymmetry (skewness statistics −1.688 and fluent in English translated the items into Catalan from the −0.307, with standard error of 0.104); moreover, task value original English version and also adapted them to fit both the showed a leptokurtic distribution (kurtosis statistic 3.440, with VET and online education contexts. A third native Catalan standard error of 0.207). Also, the violation of normality for speaker checked and standardized both translations; when doubts metacognitive self-regulation was due to multiple peaks and dips. arose, the Spanish version (Martínez and Galán, 2000) was Table 2 presents the mean and standard error values for each of consulted for the available items. In order to make the whole tool these variables. more uniform, the original response scale (a 7-point Likert scale In order to test H1, after checking the violation of normality from “Not at all true of me” to “Very true of me”) was converted assumption, Mann-Whitney U tests were performed. Finally, into a five-point frequency scale (from “Never” to “Always”). to test H2, three multiple regression models were performed, Table 2 presents the three variables, with the number of items for using the three motivational and SRL strategies as dependent each variable and an example item. Cronbach’s Alphas are also variables; student profile and program type (classroom or reported for the original Pintrich (1991) scales. online VET) were included. The stepwise method was used According to Pintrich’s (1991) model, task value is defined for each model. Independent variables (except for age) were as students’ evaluation of how interesting, how important, encoded as dummy variables (0, 1). Following Lévy and Varela’s and how useful the task and the course material are. Effort (2003) recommendation, the multiple regression model analysis regulation reflects a commitment to completing one’s study applied in this study did not aim to establish prediction, goals, even when students encounter difficulties or distractions. but to facilitate our understanding of which variables - TABLE 2 | Analyzed variables, with number of items, example items and alpha coefficient. Scale Dimension Variable No items Example item Cronbach’s Mean Alpha (Standard Error) Motivation scale Value components Task value 6 I think the course material 0.90 4.17 (0.78) in this class is useful for me to learn. Learning strategies Resource management Effort regulation 4 I often feel so lazy or bored 0.69 3.71 (0.82) scale strategies when I study for this class that I quit before I finish what I planned to do (reversed). Cognitive and Metacognitive 12 I ask myself questions to 0.79 3.33 (0.75) metacognitive self-regulation make sure I understand the strategies material I have been studying in this class. Frontiers in Psychology | www.frontiersin.org 5 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET learning mode among them- explain the different degrees of Metacognitive self-regulation levels among online VET motivational and SRL strategy employed by VET students upon students (Mdn = 3.50) differed significantly from those of commencing the course. classroom VET students (Mdn = 3.30) at the beginning of the program, U = 24116.50, z = -2.244, p = 0.025, r = -0.095. This means that at the beginning of a course, VET students enrolled RESULTS on online learning programs perceive that they employ more highly developed metacognitive self-regulation strategies than Validation of Motivational and SRL those students enrolled on classroom learning programs. The Strategies Scales data show a small effect size. After testing the first model, simulating Pintrich’s (1990) original, Effort regulation levels among online VET students the results suggested that the latent factor of metacognitive self- (Mdn = 4.00) differed significantly from those of classroom regulation had small regression weights on Items 1 and 8 (for VET students (Mdn = 3.50) at the beginning of the program, Item 1, γ = 0.159 with p < 0.0001, and for Item 8, γ = 0.066 U = 18745.50, z = -5.623, p < 0.001, r = -0.239. This means with p = 0.151). After analyzing item content and wording, we that at the beginning of a course, VET students enrolled on noticed that the two questions had a similar structure: both online learning programs perceive that they employ more were reversed and had the word “often” in them. We therefore highly developed effort regulation strategies than those students decided to eliminate these items from the analysis, since they enrolled on classroom learning programs. The data show a did not have the same effect as the originals, possibly due to the small effect size. translation into Catalan, which led to changes in the structure Considering the mean comparison, we refuted H1a and of the sentences. confirmed H1b and H1c. Following this readjustment, the final model improved the χ2 (from χ2 = 921,387 to χ2 = 573.409). Other fit indices appeared Variables That Explain Degrees of to be marginally acceptable (CMIN/DIF = 3.434; CFI = 0.910; Motivational and Self-Regulated TLI = 0.898; RMSEA = 0.065). Figure 1 presents the standardized Learning Strategies Employed by estimates and correlations among factors, and Table 3 shows the Vocational Education and Training non-standardized regression weights. The analysis of the standardized residual covariances revealed Students some local misfits (by detecting some values |1.96|), particularly The study considered the second hypothesis: H2. The learning affecting the latent factor effort regulation. Likewise, the mode chosen by VET students is a key variable when explaining modification indexes suggested freeing the regression weights the degree to which they employ motivational and SRL strategies on effort regulation items from other factor items. These when starting the program. The statement was based on the idea suggestions were not considered since the theoretical model that if online learners need to employ more motivational and SRL did not support them, however, a revision of these items is strategies in order to succeed academically, then it is important to highly recommended. determine which variables -learning mode among them- explain Finally, Cronbach’s Alpha was α = 0.844 for metacognitive self- the different degree to which VET students employ motivational regulation; α = 0.665 for effort regulation; and α = 0.902 for task and SRL strategies at the beginning of the course. value. Again, these results showed the need to improve the effort To this end, we executed a multiple regression model using regulation scale, whereas the other two scales can be considered the motivational and SRL strategies as dependent variables. The sufficiently reliable for group-level analysis. independent variables were: student profile—age, male student, The final instrument is available at https://ddd.uab.cat/record/ female student, pathways, having prior work experience, having 215267. prior work experience related to the VET program; classroom as learning mode; main reason for enrolling on VET program— personal interest, to find a job in this sector, to progress in Mean Differences Between Learning my professional career, to get a certificate, to demonstrate to Modes Among Vocational Education and myself that I have the ability; and the other motivational and SRL Training Students strategies not used as dependent variables. H1 established that VET students employ different degrees The first multiple regression model was executed using of motivational and SRL learning strategies according to the task value as the dependent variable. After five steps, the learning mode chosen (classroom or online)–H1a, H1b, and model obtained an adjusted R2 of 0.138. This means that H1c–. The results are presented below. although metacognitive self-regulation, effort regulation, the Perceptions of task value among online VET students motivation to progress in one’s professional career, personal (Mdn = 4.33) did not differ significantly from those of classroom interest in the content of the VET program, and having VET students (Mdn = 4.33) at the beginning of the program, professional experience (in general) were variables in the U = 28674.50, z = -1.287, p = 0.198, r = -0.054. This means resulting model, they only explained 13.8% of perceived task that VET students enrolled on online learning programs have value among VET students when starting a program. Table 4 the same perception of task value as those students enrolled on shows the coefficients of the resulting model, in which the classroom learning programs. chosen learning mode did not emerge as a significant factor Frontiers in Psychology | www.frontiersin.org 6 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET FIGURE 1 | Final CFA model (standardized estimates). Frontiers in Psychology | www.frontiersin.org 7 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET TABLE 3 | Final CFA model (non-standardized regression weights). TABLE 4 | Multiple regression model coefficients, using Task value as the dependent variable. Item Latent factor Estimate S.E. C.R. B SE B β TV6 Task value 0.964∗∗∗ 0.037 26.198 TV5 Task value 0.856∗∗∗ 0.040 21.483 Step 1 TV4 Task value 0.960∗∗∗ 0.036 26.597 Constant 3.16 0.18 TV3 Task value 1.000 Metacognitive self-regulation 0.31 0.05 0.29∗∗ TV2 Task value 0.910∗∗∗ 0.034 26.403 Step 2 TV1 Task value 0.627∗∗∗ 0.046 13.740 Constant 2.82 0.21 MR2 Metacognitive self-regulation 0.984∗∗∗ 0.082 11.984 Metacognitive self-regulation 0.24 0.06 0.23∗∗ MR3 Metacognitive self-regulation 0.610∗∗∗ 0.069 8.876 Effort regulation 0.15 0.05 0.16∗ MR4 Metacognitive self-regulation 0.978∗∗∗ 0.083 11.782 Step 3 MR5 Metacognitive self-regulation 1.000 Constant 2.84 0.21 MR6 Metacognitive self-regulation 1.086∗∗∗ 0.088 12.315 Metacognitive self-regulation 0.22 0.06 0.21∗∗ MR7 Metacognitive self-regulation 0.870∗∗∗ 0.079 11.047 Effort regulation 0.15 0.05 0.16∗ MR9 Metacognitive self-regulation 0.643∗∗∗ 0.069 9.347 Reason to enroll: To progress in my professional career 0.21 0.09 0.12∗ MR10 Metacognitive self-regulation 0.844∗∗∗ 0.073 11.565 Step 4 MR11 Metacognitive self-regulation 1.019∗∗∗ 0.088 11.612 Constant 2.69 0.21 MR12 Metacognitive self-regulation 0.988∗∗∗ 0.083 11.917 Metacognitive self-regulation 0.23 0.06 0.22∗∗ ER4 Effort regulation 1.591∗∗∗ 0.205 7.763 Effort regulation 0.15 0.05 0.16∗ ER3 Effort regulation 1.000 Reason to enroll: To progress in my professional career 0.30 0.09 0.16∗ ER2 Effort regulation 1.672∗∗∗ 0.216 7.756 Reason to enroll: Personal interest 0.24 0.09 0.14∗ ER1 Effort regulation 1.154∗∗∗ 0.180 6.421 Step 5 Constant 1.49 0.55 ∗∗∗ p < 0.0001. Metacognitive self-regulation 0.23 0.06 0.22∗∗ Effort regulation 0.15 0.05 0.16∗ in explaining perceived task value among VET students when Reason to enroll: To progress in my professional career 0.29 0.09 0.16∗ starting a program. Reason to enroll: Personal interest 0.24 0.09 0.14∗ When using metacognitive self-regulation as a dependent I have professional experience (in general) 1.21 0.52 0.11∗ variable, four steps were needed to obtain an adjusted R2 of 0.228. Adjusted R2 = 0.083 for Step 1, 1R2 = 0.019 for Step 2 (p = 0.003), 1R2 = 0.011 The variables effort regulation, task value, not having a personal for Step 3 (p = 0.015), 1R2 = 0.014 for Step 4 (p = 0.007), 1R2 = 0.010 for Step interest in the content of the VET program (negative correlation) 5 (p = 0.019), ∗∗ p < 0.001 ∗ p < 0.05. and being female were key variables that only explained 22.8% of perceived metacognitive self-regulation in VET students. Again, any professional field (Brennan et al., 2001). Yet, not all students the chosen learning mode was not one of the significant variables succeed in online VET. Studies have evidenced that they must in the model. Table 5 presents the coefficients of this model. be highly motivated and able to regulate their own learning The last regression model took effort regulation as the (Garrison, 2003; Artino and Stephens, 2009). Our research aimed dependent variable. The resulting model obtained an adjusted to analyze the degree to which VET students employ motivational R2 of 0.246 in only three steps. This model comprised and SRL strategies, focusing on the two VET modes, online and metacognitive self-regulation, student age, and task value. These classroom, in order to understand how students’ characteristics three independent variables were able to explain 24.6% of the are related to their engagement in the course. variance in the effort regulation variable, a higher percentage than the other three models. Table 6 offers the coefficients of the Validating Task Value, Effort Regulation model obtained. Again, the chosen learning mode was not a key and Metacognitive Self-Regulation variable in this model. The results from all three models refuted the second Scales in New Contexts hypothesis (H2). Regarding the first aim of the research, some evidence of construct validity was obtained for the translated and adapted version of Pintrich’s (1991) scales for analyzing metacognitive DISCUSSION self-regulation, task value, and effort regulation among VET students in Catalan. The evidence suggests that motivation and SRL strategies are Our results showed that correlations among factors were quite important in determining academic success in any educational similar as with the original model constructed by Pintrich (1991). stage and process, including in VET studies (Wang et al., 2013; This means that Pintrich’s model is also suitable for analyzing Alkis and Taskaya, 2018). Online learning environments open up motivational and SRL strategies used by VET students both new avenues for VET students because they are not seen merely as online and on classroom programs. tools to support learning, but as dynamic settings that are flexible, Despite the similarities with Pintrich’s (1991) original findings, attractive and interactive, and make lifelong learning possible in the results for validity and reliability indicated that a revision of Frontiers in Psychology | www.frontiersin.org 8 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET TABLE 5 | Multiple regression model coefficients, using Metacognitive Despite the limitations mentioned, this step forward in the self-regulation as the dependent variable. validation of scales in itself represents an important achievement B SE B β that has not been reported previously, because it enables Catalan educational institutions to apply the model to all types of VET Step 1 studies. This will provide VET instructors with the tools to Constant 1.973 0.161 evaluate the motivational and SRL strategies employed by their Effort regulation 0.366 0.042 0.408∗∗ students and better adapt their teaching methodology to them. Step 2 Constant 1.341 0.215 Effort regulation 0.320 0.042 0.357∗∗ Identifying Differences Between Task value 0.192 0.044 0.203∗∗ Learning Modes Step 3 In relation to the second aim of the study, namely, to Constant 1.411 0.214 identify differences in students’ motivational and SRL strategies Effort regulation 0.304 0.042 0.338∗∗ depending on the learning mode they enrolled on (classroom or Task value 0.199 0.044 0.211∗∗ online), the results of the Mann-Whitney test were relevant. Reason to enroll: Personal interest −0.253 0.074 −0.153∗ Our findings pointed to the fact that VET students enrolled Step 4 on online learning programs perceived they have more highly Constant 1.444 0.213 developed metacognitive self-regulation and effort regulation Effort regulation 0.293 0.042 0.326∗∗ strategies than those enrolled on classroom learning programs. Task value 0.195 0.044 0.206∗∗ This aligns with the results of other studies (Martínez and Galán, Reason to enroll: Personal interest −0.254 0.074 −0.154∗ 2000; Artino and Stephens, 2009), which showed how important Female 0.134 0.067 0.090∗ SRL strategies are in explaining students academic success, Adjusted R2 = 0.164 for Step 1, 1R2 = 0.037 for Step 2 (p < 0.001), 1R2 = 0.021 specifically in online learning (Triquet et al., 2017). Thus, having for Step 3 (p = 0.001), 1R2 = 0.006 for Step 4 (p = 0.047), ∗∗ p < 0.001 ∗ p < 0.05. these strategies when starting a VET program in online mode becomes essential to their success. The last meta-analysis study TABLE 6 | Multiple regression model coefficients, using Effort regulation as the published by Lee et al. (2019) showed an alignment between of dependent variable. our results and those of several studies on online learners’ success factors and the importance of self-efficacy and SRL strategies. In B SE B β respect of this, Magen-Nagar and Cohen’s (2017) study proved Step 1 that learning strategy was a significant mediator for motivation Constant 2.252 0.177 and academic achievement among online high-school students, Metacognitive self-regulation 0.454 0.052 0.408∗∗ which constitutes an important contribution to understanding Step 2 the implications of our results. Constant 1.837 0.183 Despite what we might have expected, no differences were Metacognitive self-regulation 0.402 0.050 0.361∗∗ found between online and classroom VET students when it Age (in years) 0.022 0.004 0.275∗∗ came to perception of task value. This indicates that online Step 3 and classroom VET students perceived the value of the task Constant 1.465 0.233 -the course they enrolled on- to be equally important, even Metacognitive self-regulation 0.366 0.052 0.329∗∗ though task value has been proven to be one of the most Age (in years) 0.021 0.004 0.263∗∗ important motivational factors, it being associated with learning Task value 0.124 0.049 0.117∗ engagement and success (Aristeidou et al., 2017; Zhang and Liu, 2019) and a significant predictor of course completion Adjusted R2 = 0.164 for Step 1, 1R2 = 0.072 for Step 2 (p < 0.001), 1R2 = 0.011 for Step 3 (p < 0.001), ∗∗ p < 0.001 ∗ p < 0.05. (Cho and Heron, 2015; Vanslambrouck et al., 2018). As Cents- Boonstra et al. (2018) showed, VET students with a higher level of motivation also have a higher level of self-efficacy, which is the effort regulation scale is needed. Dunn et al. (2012) faced strongly linked to learning success. similar problems when conducting the statistical revaluation of Considering these results, H1a was refuted and H1b and effort regulation and metacognitive self-regulation scales. Thus, H1c were confirmed. In other words, there were no differences more analyses are required to confirm the goodness of fit of in perception of task value between online and classroom SRL scales among VET students in Catalonia, since difficulty and learning VET students, which refuted H1a; but there were discrimination parameters may have changed. It is crucial that differences in perceptions of the metacognitive self-regulation more data are gathered to validate the model in a new context and effort regulation learning strategies employed between these like the current one. In addition, a revision of the translation two groups, which confirmed H1b and H1c, online learning and adaptation of Items 1 and 8 is required in order to improve VET students perceiving their self-regulated learning strategies them and include them in the model, which would make the to be more developed. results of the Catalan version of the scale more comparable The fact that there were no differences in task value perception with the original. between online and classroom VET students reveals an important Frontiers in Psychology | www.frontiersin.org 9 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET area for improvement for educational institutions. We expected considering the small size of the Catalan-speaking community. online students to have a higher perception of task value than However, given the similarity of the VET system implemented classroom students because their learning process is based on in all Spanish regions—and also in some Latin-American a more student-centered and autonomous model and they countries—the results could be interesting for other Spanish therefore need more self-motivation. Furthermore, online course communities, and the validated tool could easily be adapted satisfaction and its connection with student motivation were also to their context. Nevertheless, the specific context of the study found to be related to academic success in this study (Herbert, generated one important limitation: the difficulty of comparing 2006), while Lee et al. (2019) review showed that task value and these results with other studies in the field. Another limitation SRL strategies positively affect a sense of academic achievement, is related to the non-random study sample, which, despite motivation and learner behaviors. Online VET instructors and having an adequate number of participants, meant caution was course designers could use this knowledge to improve their required when attempting to generalize the results. A further programs and thereby foster motivation and success among VET limitation was the validation indices for the scales, which were students. Indeed, increasing online VET students’ perception of acceptable but not optimal; more research will be needed to task value by providing meaningful learning environments could improve the scales. improve students’ motivation and academic success (Radovan Due to the little available research on VET students’ and Radovan, 2015; Dubeau et al., 2017). motivation and the SRL strategies they employ (Savoie- Approaching these differences by applying multiple regression Zajc et al., 2010), our findings could make an interesting models that use the factors posited by Pintrich as dependent contribution to the field. variables, our findings suggest that more than 75% of the Several researchers have suggested (e.g., Cardinal, 2010; variance of all three models still remains unexplained with Cents-Boonstra et al., 2018; Dubeau et al., 2017; Biemans et al., the profile variables included. Thus, the regression models 2019) that VET programs attract less motivated students, and obtained do not include all of the variables we need to help studies on VET motivational and SRL strategies are therefore us understand which motivational and SRL strategies play an not priority areas for exploration. To us, this opens a door to important role when selecting the mode of VET learning. One new research, which might focus on the Catalan context using way to acquire fully explained models could be to use Pintrich’s the preliminary validated scales to compare motivation levels (1991) complete model; while others could be to measure other and SRL strategies between VET and high-school or university profile variables -such as time spent studying course materials, students—these being similar in age but differing in levels of family responsibilities- and cognitive variablessuch as working motivation (Martin, 2008). memory (Torrano and González Torres, 2004), and designing This article presents only one measurement of motivation longitudinal studies such as the work done by Lee (2015) using and SRL strategies, which could be viewed as a limitation in mixed-methods to gain more in-depth knowledge of the reasons terms of how far our results contribute to VET research. That behind the results obtained. said, however, we strongly believe that longitudinal studies are It is also interesting to note that the independent variable also a missing piece of the puzzle we have started to construct learning mode classroom or online that students enrolled on for in here. Hence the project extending beyond this paper and the VET program was not significant in explaining the degree including the collection of the same measures over the entire of motivational and SRL strategies employed by these students. 2-year program; other qualitative techniques will be added to Results from the two models allow us to refute our second gather as much information as possible to obtain a clearer picture hypothesis (H2), because we were not able to find any evidence to of VET students’ motivation and SRL strategies. Our aim is to support the idea that the learning mode chosen by VET students determine whether our results are maintained over time, as Lee’s was a key variable when explaining the degree to which they (2015) study did using a longitudinal sample. employ motivational and SRL strategies. In line with this, Lee (2015) has already pointed out that “when This means that even though there were significant differences measurement on students’ motivation is available in the early between these type of students classroom and online in the degree stages of a semester, some interventions can be implemented to to which they employed metacognitive self-regulation and effort foster their motivations, thus preventing their dropping out of regulation when beginning a VET program, this difference might class” (p.63). Knowing for a fact that this is also the case with not be entirely explained by their choice of learning mode. This Catalan VET students (both in classroom and online learning idea can only be tested by conducting longitudinal studies during mode), educators can design and implement advanced tasks that their learning period in VET programs similar to the work done engage students from the very first, since students’ extensive previously by Lee (2015) or Throndsen (2011); using various employment of motivational and SRL strategies ensures a high methodological approaches may cover qualitative aspects of their probability of course completion and therefore academic success responses that quantitative methodology cannot capture. (Cho and Heron, 2015; Vanslambrouck et al., 2018). Limitations and Future Directions Some limitations were identified when considering the DATA AVAILABILITY STATEMENT implications of this study. The fact that the research was conducted in the Catalan VET context and the tool we applied The datasets generated for this study are available on request to was in the Catalan language can be seen as a limitation, the corresponding author. Frontiers in Psychology | www.frontiersin.org 10 December 2019 | Volume 10 | Article 2795 Quesada-Pallarès et al. Online Learning in VET ETHICS STATEMENT database. CQ-P and AS-M wrote the first draft of the manuscript. CQ-P, AS-M, AC-C, and PP-H wrote sections of the manuscript. The studies involving human participants were reviewed and All authors wrote sections of the manuscript, contributed to the approved by the Universitat Internacional de Catalunya. Written manuscript revision, read, and approved the submitted version. informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements. 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