This study aimed to identify the effects of e-learning environment of Google Classroom in relation to cognitive styles (independence/dependence) on developing college students' English vocabulary learning and instrumental motivation as well as deciding on the correlations between students' vocabulary learning, instrumental motivation, and cognitive styles. Participants in this study comprised 117 randomly selected EFL juniors, assigned to two experimental groups classified according to their cognitive styles (independence vs dependence). Instruments of the study included the English Vocabulary Retention and Retrieval Test (EVRRT), a cognitive styles inventory (GEFT) for identifying participants’ cognitive styles, and the Instrumental Motivation for Achievement Scale (IMAS). The study revealed that there were statistically significant differences between the mean scores of the experimental group with context-independent cognitive styles and the experimental group with context-dependent cognitive styles to the good of the former group on vocabulary retention on post-testing. In addition, statistically significant differences between the mean scores of the experimental group with context-independent cognitive styles and the experimental group with context-dependent cognitive styles to the good of the former group on post-testing of instrumental motivation. Overall, there was no correlation between vocabulary retention as a function of using the Google Classroom e-learning environment, and motivation mean responses on post-testing.
Research Article
Using E-learning environments in relation to cognitive styles for developing college students' English vocabulary learning and motivation
https://doi.org/10.21203/rs.3.rs-3193867/v1
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E-learning Environment
Cognitive Styles
instrumental motivation
English vocabulary
EFL college students
In and after the time COVID-19 has stricken the world, the need for using e-learning environments functionally for course delivery and interaction with learners loomed large. Educational institutions from K-12 up to tertiary education institutions have expanded their investment in online learning to deliver synchronous and asynchronous learning for students through flexible online learning environments and platforms unaffected by boundaries of time or place. But intuition has it that online learning may be influenced by other factors such as students' personality traits, cognitive styles, attitudes or motivations – factors that need to be carefully examined.
The interaction between students' cognitive styles and the web-based learning environments has been suggested in early reported study findings to be influential in moulding students’ attitudes towards and motivations for e-learning as well as students’ academic achievement (See Araújo, Dorça, Brant-Ribeiro, Cattelan, & Ferreira, 2020; Sabry & Baldwin, 2003). Later studies showed that transitioning from in-person to online learning has started to impact student populations in pre-university and university education institutions, especially as regarding their cognitive styles and motivations (Nizam, Norazizi, Kasman, Nazaruddin, Samat & Muda, 2021; Olivos, Santos, Martín, Cañas, Gómez-Lázaro, & Maya, 2016).
Prior research also suggested that learners in the same environment tend to demonstrate different cognitive styles, levels, and quality of learning as well as use different approaches and methods of language learning and language use (Bhatia, 2011; Lal, 2015; Salawu, Azeez, & Madikizela, 2022), notably in foreign language learning. Learning styles, the preferred ways individuals interpret new knowledge and learn, are essential for students and teachers to help the former understand how they learn and retain and retrieve what they learn, as well as induce significant improvement in their academic achievement when students are taught according to their learning styles (Williamson & Watson, 2006). Identifying and analysing student motivation could also be as beneficial as cognitive styles, as motivation interacts with educational choices, students' achievement behaviours and the characteristics of educational settings (Bandura, 1997; Hattie, 2009; O'Brien, 1994; Vaishnav & Chirayu, 2013). Some researchers showed that an adaptive learning environment could positively impact students' cognitive styles and motivations in ways that could improve students' academic achievement (Bhatia, 2011; Lal, 2015; Singh, Sharma & Shukla, 2023). In addition, understanding the relationship between cognitive styles, motivation, and academic achievement could implement the curriculum and educational programmes more successfully, executable, and updatable (Nizam et al., 2021).
Furthermore, a learning environment, including online learning settings, should aim to develop an integrated learner personality so that one's cognitive, affective, and psychomotor dimensions of development can be attended to when designing e-learning. As such, individual differences in learning potentials, cognitive styles and motivation need to be identified, and students could be situated in these environments categorically by the variation of these factors. Learner participation in active, interactive online environments is the outcome of the interaction between one's cognitive styles, instrumental motivation, and attitudes, thus making learner participation an influential factor and critical element in successful e-learning (Çebi & Güyer, 2020; Khan, 2005; Küçük, Genç-Kumtepe & Taşcı, 2010; Wu, He, Li, Han, & Huang, 2022; Thompson & Savenye, 2007).
Field-independent versus field-dependent cognitive styles, in this regard, have been identified as more common in existence by learners; field-independent learners are characterised by the ability to perceive the parts of the subject and its various details separately from the structure of the subject as a whole, while the field- dependent learners are characterised by their comprehensive organisation ability to look into the subject as a whole. Therefore, a cognitive style of field-dependence versus field independence affects the outcomes of teaching and learning, especially from a cognitive perspective, namely, students' academic achievement as the main output of the learning process (e.g., van de Wege, 2018). Instrumental motivation was also considered a key element when designing online learning environments (Hartnett, George & Dron, 2011; Meşe & Sevilen, 2021). Instrumental motivation has been linked to better achievement in a quality online learning environment), especially in foreign language learning (Collis & Moonen, 2001; 2006; Zhao & Shang, 2022).
When it comes to vocabulary learning and mastery as a fundamental part of foreign language learning, mobile technologies and the relevant online learning environments associated with these technologies were suggested as alternative milieus that would ‘enable students to learn vocabulary without space and time limitations’ (Wang, Hwang, Yin & Ma, 2020, p. 16).
The underlying notion for the present study has been to investigate the impact of the online learning environment of Google Classroom as a flexible, easy-access and readily available mobile application on the interaction between cognitive styles, instrumental motivation for achievement and development of EFL vocabulary in EFL college students.
EFL college students in the Faculty of Education at Beni Suef have been recognised as weak language learners, especially in vocabulary learning and writing skills, thus needing to be more motivated to learn more vocabulary. That they were weak in language skills and vocabulary learning has been identified in their performance tests in language skills courses. Language instructors, informally interviewed, also indicated that the students exhibited low proficiency levels and low aptitude for learning, mainly attributed to low vocabulary range and weak grammatical competence.
Research questions
The main research question that informed the present study was:
What is the effect of the interaction between the electronic learning environment and the College of Education students’ cognitive styles on developing their writing skills and improving motivation?
The following sub-questions emanated from the above research question, below stated:
- What is the effect of using E-learning environment in relation to cognitive styles to develop college students' vocabulary learning?
- What is the effect of using E-learning environment in relation to cognitive styles to develop college students' motivation?
- What is the correlation of college students' vocabulary learning and cognitive styles?
Aims of the Study
This study sought to:
- identify the effects of e-learning environment of Google Classroom in relation to cognitive styles (independence/dependence) on developing college students' English vocabulary learning.
- identify the effects of e-learning environment of Google Classroom in relation to cognitive styles (independence/dependence) on developing college students' instrumental motivation; and
- deciding on the correlations between students' vocabulary learning, instrumental motivation, and cognitive styles.
Significance of the Study
- EFL college students are expected to improve their English vocabulary learning when they study English vocabulary courses in online learning environments, considering their cognitive styles.
- EFL college students are expected to enhance their instrumental motivation when they study English vocabulary courses in online learning environments.
- Identifying which type of cognitive style is influential in helping students learn English vocabulary in online learning environments and deducing pedagogical implications for teaching and learning EFL skills.
- Course-designers are expected to use findings from this study and similar ones in redesigning vocabulary courses to be delivered in online learning environments, such as Google Classroom and other platforms as Edmodo® or Blackboard®.
- Findings from this study could help EFL college teachers in e-course planning, presentation, and evaluation in online learning environments.
Online learning environments
Online learning environments have emerged as a supportive and sometimes alternative environment for learning since 2001 (Olivier & Liber, 2001). Kumar Basak, Wotto & Bélanger (2018) explained that online or e-learning, mobile learning (m-learning) and digital learning (d-learning) are used interchangeably or complementarily to mean technological learning, which emerged as the alternative to traditional education or could be used to complement it, integrate it or replace it. E-learning supports traditional learning through electronic tools and media (Hoppe, Jointer, Milrad & Sharples, 2003). It is facilitated in online learning environments by digital technology, which facilitates instructional practice and effectively uses technology for learning (Kumar Basak et al., 2018).
As such, an online learning environment is defined as a platform, a design or a programme with an interface that allows learners to access and interact with the e-course - a digitally designed syllabus and material for learning and learner evaluation uploaded to a secure, login-supported environment (Müller & Strohmeier, 2010). It was also recognised as a software designed to manage the learning process and regulate synchronous and asynchronous learning and interaction between teachers and learners as well as learners and learners. Therefore, such platforms are described as learning management systems with virtual learning supports and tools made available online through login credentials for free or a paid subscription (Arkorful & Abaidoo, 2014; Weaver, 2008).
An e-learning environment is a virtual place for modifying and changing learners' behaviours and performance using a specific learning software or a platform to which an e-course is uploaded. Therefore, such an environment is made up of three main components: 1) individual performance, including learners, teachers and peers or teamwork; 2) learning materials, including videos, pdfs, graphics and other technologically designed learning material; and 3) the interaction between individuals in this environment through messaging, discussion, debates, commentary and opinion sharing, all of these being practised in a computerised environment involving hi-tech programmes and e-course management systems (Rashad, 2010).
Preliminarily, e-learning was used as a supportive alternative to traditional classroom learning until soon when the COVID-19 pandemic struck the world and made resorting to e-learning a must (Al-Oteibi & Al-Abdelli, 2020; Othman, Al-Allawendi, & Farahat, 2020). Researchers and educational practitioners could adduce that such environments, like Google Classroom, a free e-learning platform or Blackboard® or Edmodo®, two paid platforms, could enable course designers and instructors to modify e-course content at low costs and efforts, boost the instrumental motivation for learning and achievement, provide learners with the opportunity to be self-independent or peer or team-learners. Such environments also help teachers to vary the experiences of learning in virtual reality, which helps in improving learners; thinking skills and language learning skills independently of the role of the teacher. According to Khan (2005), the type of e-learning transitioned through online learning environments is open-access, distributed and flexible, lending to the moulding of an active, interactive, analytical and critical learner grounded in the learning theories of behaviourism, cognitivism and constructivism (as shown in Fig. 1 below):
Participation in e-learning environments is typically measured by the learners' activities in discussion forums and message exchanges between peers and themselves and between learners and teachers (Küçük et al., 2010). In line with the constructivist approach, online discussion forums depend on the idea that students can learn as much from one another as from course materials or instructors. In a discussion forum, a participant can have opportunities to express ideas, have them criticised, reshape ideas in light of peer discussions (Kilinc & Altınpulluk, 2021), and offer feedback on others’ ideas. This exchange of ideas and works helps students engage in higher-order information processing and construct their meanings.
Cognitive styles
Cognitive styles refer to individuals' preferences to organise and process information and learning experiences. In contrast, learning styles, a similar construct, refer to individual skills and preferences that affect how learners perceive, collect and process learning materials (Sadler-Smith, 2001). Educators used to conceive of the two constructs as synonymous, and they used them interchangeably, but both terms have nuances in meaning and use (Dörnyei & Ryan, 2017). A cognitive style is congenital and is set from birth, describing how one processes information cognitively in the brain. Thus, it is distinctively descriptive of a person. In contrast, a learner style refers to how an individual approaches knowledge and experiences and interacts with and responds to learning material in a given learning environment (Li, 2022).
A well-known and accepted theory of learning styles is that of Fleming's VARK, in which learning styles were classified into three categories: visual learners, auditory learners and kinaesthetic learners; learning styles assume that learners approach information in ways that are consistent with their preferred learning modes (Fleming & Baume, 2006). On the other hand, cognitive styles are manners or strategies people use distinctively for receiving, processing and responding to information, thus being specifically related to information processing, retrieval and retention. Cognitive styles, therefore, make for individual variations and differential aspects of learners at the levels of cognition, affect and motivation (Popa, Țepordei, Labăr & Frumos, 2018). According to Bendall, Galpin, Marrow & Cassidy (2016), cognitive styles are methods used by individuals in dealing with the stimuli they are exposed to in their different life situations, which helps to reveal the differences between individuals not only in the cognitive field such as perception, remembering, thinking, forming concepts, learning, constructing and retaining information, but also in the affective and emotional field, the social field and the study of personality; thus distinctive cognitive styles of individuals are interpreted in light of the methods of activity that they practice, regardless of the content of this activity (Panadero, 2017).
Researchers explained that cognitive styles are characteristically descriptive of a cognitive activity framework and are learning situation-related but are not restricted to the learning or experience content; therefore, cognitive styles are related to individual differences as to how to practice cognitive processing, such as thinking, perceiving, retaining and retrieving information, problem-solving and handling and developing information (Panadero, 2017). Cognitive styles are relatively stable and flexible but are not easily prone to change, which helps predict how individuals behave cognitively in learning situations and environments (Dai, 2014; Kozhevnikov, Evans & Kosslyn, 2014; Zhang, Sternberg & Rayner, 2012). However, they can generally be directed or guided as comprehensive personality descriptors. Cognitive styles can be assessed verbally or non-verbally, and they are determined by the sociocultural backdrop and the socio-educational milieu in which a person lives, suggesting that cognitive styles reveal the heuristics that people use to process information about their environment. According to researchers, cognitive styles can be classified into several bipolar styles: context dependency vs interdependency, rule-based vs intuitive processing, internal vs external locus of processing, and integration vs compartmentalisation (Kozhevnikov, 2007).
Kozhevnikov et al. (2014), reviewing the first large-scale experimental study by Witkin, Moore, Goodenough & Cox (1977), indicated that field-dependent individuals rely on the surrounding context, and field-independent individuals do not depend on their surrounding environment; therefore, researchers reported a relationship among participants' performance on perceptual tests, their personality characteristics, and their social behaviour' (Kozhevnikov et al., 2014, p. 6). Furthermore, field-dependent individuals are more observant of social cues than are field-independent individuals; in addition, field-independent individuals tend to entertain a more impersonal orientation than do field-dependent people, thus seeking to keep a psychological or even a physical distance from their peers (See Witkin & Goodenough, 1981, and Kozhevnikov et al., 2014 for a comprehensive review).
The core of cognitive styles theories is deeply grounded in an individual's innate predispositions where the opposite poles of the field-dependence/independence style dimension characterise the products of the diverse modes of physical adjustment and cognitive adaptation to the world (Kommers, Stoyanov, Mileva and Martinez, 2008). Learning styles are environmentally sensitive. Therefore, learners would exhibit individual differences in cognition and learning (Kozhevnikov et al., 2014). Early research in education investigated cognitive styles based on individual differences in perception and concept formation, the lower-order cognitive processes, while more recently, the focus has been on learning styles or the higher-order cognitive functioning such as hypothesis formation, problem-solving and modelling (Evans & Cools, 2011; Evans & Waring, 2013; Evans & Kozhevnikov, 2013; Rayner & Cools, 2011; Peterson, Rayner, & Armstrong, 2009).
Educational research also provided empirical evidence indicating that the learning environment influences the poles of dependency versus independency of context. For instance, the context-independent learner is more capable of analysing situations, retrieving information, and organising the elements of learning material. The locus of control is internal when dealing with the elements of the external situation, and where an individual prefers learning through written or oral verbal language and gets high scores on tests that depend on perception and understanding. However, a context-dependent learner is characterised by full awareness of the learning material, prefers to deal with topics that are presented in an orderly manner, and also prefers learning through audio-visual materials, thereby tending to get lower scores than the context-independent learner, especially on tests that rely on memorisation and understanding, as such individuals fail to deal with too much information (Kollöffel, 2012; Kommers et al., 2008; Menaker & Coleman, 2007).
Several tests contribute to the classification of individuals according to the cognitive style of context-independence versus context-dependence; for example, Witkin, Dyk, Faterson & Karp (1962) introduced the Group Embedded Figures Test (GEFT), which assesses the field-dependence/independence dimension, an Arabic version of the Group Embedded Figures Test standardised by Al-Sharkawi (Version V, 2013) was used in the present study to classify the sample into context-dependent and context-independent participants. The GEFT inventory is comprised of three sections; section (1) training, yet the score of this section is not calculated in the examinee's assessment, and it consists of seven easy ways to do paragraphs; section (2) consists of nine paragraphs of graded difficulty; and section (3) consists of nine paragraphs of graded difficulty as well. This section is considered equivalent to the second section of the GEFT. Each paragraph in the three sections is a compound form with a simple form inside. The learner is asked to define the boundaries of the simple form inside the complex shape in pencil, and the simple shapes that the learner is asked to discover and set their borders are printed on the last page of the GEFT. The GEFT is designed in a way that the learner cannot see the simple form within the complex form that it contains at the same time, and each section of the test has a specific time to do: two minutes for the first section, five minutes for the second section, five minutes for the third section. The researchers restricted the test timing and the answer key associated with the Arabic version of the GEFT.
Instrumental motivation
Instrumental motivation, also called motivation for achievement, is a type of motivation that drives an individual to perform a specific action or behaviour in order to achieve a particular goal or reward. It is a form of extrinsic motivation, comprising four essential components, namely: 1) self-confidence, including one's preparedness and predispositions, and the potential for work, which qualify one to achieve their goals and accomplish the tasks entrusted to them; 2) mastery for perfection that requires perseverance and effort individually pursued without relying on others; 3) independence or the ability to work independently of others, grounded in one's belief in their abilities; and finally 4) ambition and striving for excellence (Mattar, 2013).
According to Lens, Paixão, & Herrera (2009), instrumental motivation is generally sought after in educational settings. It is correlational or cross-sectional, where learning is not a goal per se, but an instrumental activity in the learning environment is what makes this type of motivation matter; individuals entertain instrumental motivation because it will have positive consequences in the future, such as to be able to adapt to the learning environment or pass exams (Lens et al., 2009, p. 24). Unlike cognitive styles, instrumental motivation is not a static construct; it can be developed and changed across domains, contexts, or activities (Nizam et al., 2021; Ryan & Deci, 2000). Prior research demonstrated " a positive and significant correlation between academic motivation and academic achievement" (Nizam et al., 2021, p. 280).
Vocabulary learning: retrieval and retention
The role of vocabulary in EFL learning is indispensable and indisputable, with vocabulary being the flesh of the language system (Wang, Gwo-Jen Hwang & Ma, 2020). Harmer (1991) argues that "If language structures make up the skeleton of language, then it is vocabulary that provides the vital organs and the flesh" (p. 153). Vocabulary learning represented in the interdependent cognitive processes of retrieval and retention is significantly difficult (Chen & Chung, 2008; Ebbihaus, 1913); however, if the learning environment contains images or graphics, videos and other mnemonics, vocabulary learning tends to transition from the short-term working memory to the long-term memory (Driscoll, 2005). However, conventional vocabulary teaching/learning is often restricted in terms of both class time devoted and effective retention strategies used since learners need to primarily be self-dependent in their vocabulary learning - a process that possibly poses problems and frustration for learners, eventually resulting in their loss of motivation (Dörnyei & Csizér, 2002).
Therefore, the literature review suggests using online learning environments that capitalise on multimodal presentation and spaced repetition to help students successfully learn vocabulary, retrieve it and retain it when needed (Kohnke, 2019). The use of multimedia entails lexical understanding and retrieval, as suggested in the Cognitive Theory of Multimedia Learning (Kohnke, 2019; Wang et al., 2020). Additional research is yet needed to explore this effect of online learning environments on vocabulary learning and hence the rationale for conducting this study.
Hypotheses
This study sought to validate the following hypotheses:
-
There is no statistically significant difference (p ≤ 0.05) between the mean scores of the experimental group with the context-independent cognitive styles and the experimental group with the context-dependent cognitive styles on post-testing of the language vocabulary achievement test.
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There is no statistically significant difference at the (≤ 0.05) level between the mean scores of the experimental group with the context-independent cognitive style and the experimental group with the context-dependent cognitive style on post-testing of the instrumental motivation scale.
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There is no correlation between developing English vocabulary and instrumental motivation.
Method
The researchers in this study employed the semi-experimental research method to know the effect of the two independent variables. The first variable is experimental, represented in the electronic learning environment (Google Classroom with the vocabulary course designed and uploaded to this e-learning environment), and the second is classifying, represented in the cognitive styles according to which the sample has been assigned. Two groups of participants were chosen based on their classification by cognitive styles; the first group is context-independent, and the second is context-dependent to explore the effects of Google Classroom as an e-learning environment on the dependent variables (English language vocabulary and instrumental motivation).
Design
The researchers in this study employed a pre-test, post-test design with two experimental groups, the first having context-independent cognitive styles. In contrast, the second group was identified as having context-dependent cognitive styles.
Participants
The participants in this study were all third-year students enrolled in the first semester of the EFL bachelor in TEFL, from which two samples were drawn, totalling up to 117 students of both genders. The sample has been regrouped by their cognitive styles using the GEFT inventory standardised Arabic version into two groups – the first experimental group (N = 60) with context-independent cognitive styles and the second experimental group (N = 57) with context-dependent cognitive styles.
Experimental model
The ADDIE model was used as the instructional design of teaching and learning in the Google Classroom e-learning environment, being a customarily flexible design of instruction used to develop courses and streamline the production of instructional material. Researchers suggested the ADDIE instructional design works better for learning environments structured with frameworks for continuous quality improvement, such as the Google Classroom online environment (Allen, 2006; Yeh & Tseng, 2019). The model consists of five primary stages, providing feedback at each stage, as shown in Fig. 2 below.
The following is a detailed description of each stage of the model.
1. The analysis includes the following steps:
Analysis of the characteristics of learners:
The present study targeted university students, all of whom have experience using computers and the Internet. They have some weakness in correctly using language vocabulary due to the inability to retain or retrieve it, which negatively affects their motivation towards learning and achievement (instrumental motivation). Such students entertain enthusiasm for using effective strategies associated with online learning environments during the learning process to develop their language skills.
Analysis of learning needs:
Learning needs in this study were recognised in the development of English language vocabulary in EFL college learners and their instrumental motivation through an electronic learning environment (Google Classroom) and its impact on vocabulary achievement as a result of effective retrieval and retention of vocabulary sub-skills.
Analysis of resources and capabilities:
The researchers ascertained the availability of the required educational resources, methods and tools that can be used to achieve the objectives of the present study to help learners develop their vocabulary.
2. The design includes the following steps:
Formulation of instructional objectives
Based on the general goal of the study, which aimed to develop EFL college students' vocabulary and investigate the interactions between online learning environment, cognitive styles and instrumental motivations, a set of instructional objectives were derived and formulated in measurable behavioural statements. (See appendices).
Deciding on the vocabulary learning content
The elements of the vocabulary learning content were identified based on the intermediate level of vocabulary grounded in Roberts, Clare and Wilson's Total English (Students' Book with ActiveBook plus Vocabulary Trainer) and consistent with standards of the English programme curriculum of the first year of TEFL teacher education in the College of Education, Beni Suef University. The researchers divided the content units into small parts compatible with the previously identified instructional objectives and presented them sequentially and coherently, which is easy to understand.
Specifying the teaching/learning strategies
The e-learning environment of Google Classroom was developed based on self-directed learning strategies for learning the educational content where each learner had to view and interact with the target vocabulary, taking into account the provision of vocabulary learning lessons and elements that attract students such as integrated pictures, graphics, texts, and sounds as well as pdfs and video mashups.
Specifying multimedia elements:
In this step, the media elements included in the learning content and provided via the learning environment have been identified, including varied images, video clips and texts, considering the interaction and integration of these elements to achieve the e-course objectives.
Defining interactions:
These included the learners’ interactions with the interface of the electronic environment, interactions with the content provided, and interactions with the teacher and colleagues.
3) Development:
At this stage, the materials and multimedia previously identified in the design stage were developed by making the advanced materials, tools and assessments available or by modifying and reproducing the material to make it adaptive to the students ‘needs.
4) Implementation:
During this stage, the e-learning environment represented in Google Classroom was built, the general goals were set, and then links to learning units were created and sent to the learners to partake in learning in the online environment.
5. The evaluation included the following:
Continuous evaluation:
Evaluation occurs at each stage and step of the instructional ADDIE design to ascertain the quality and infallibility of learning materials and presentations. This stage also included formative evaluation to help students learn from their mistakes in vocabulary retention and retrieval.
Summative evaluation:
This evaluation was achieved by sending out the learning environment-compatible instructional design and the teaching/learning content to five jurors in educational technology and TEFL methodology to ensure their technical and methodological adequacy. It was piloted through a survey administered to 25 students other than the study participants to ensure ease of use, the learners’ ability to interact with the interface, and the clarity of the content. It should be noted that the learners indicated that there was no difficulty in using the e-learning platform of Google Classroom and that the environment was ready for utilisation.
Instruments
The English Vocabulary Retention and Retrieval Test (EVRRT) took the form of active recall, asking students to provide the missing word in context-related sentences, reorder words to form fully meaningful sentences, and use words in contexts that show their meanings. In random orders, these same items were tested in a pre-, post- and delayed-test fashion. Two raters first scored the tests independently and then discussed those with differing opinions until they reached an agreement. The final analysis of data only included items that the students illustrated.
The Instrumental Motivation for Achievement Scale (IMAS) was developed based on an exhaustive review of relevant literature, and it was comprised of six dimensions: 1) perseverance; 2) ambition; 3) competitiveness; 4) self-confidence; 5) future-orientedness; and 6) rewarded ness, each of which constitutes six items, with IMAS totalling up to 36 items in whole. The IMAS was checked for face and construct validity by inter-raters and external jurors for the relevance of items, language accuracy, measurability and suitability for the age category. It was also piloted to assess its internal consistency and reliability. Table 1 below shows the reliability of the IMAS:
Correlations between components of the IMAS and its constituent items ranged between 0.73 and 0.92, all being significant at 0.01, indicating that the IMAS was internally consistent. A Cronbach alpha coefficient was computed, indicating that the IMAS was reliable at 0.94 - a high-reliability coefficient.
Experimental procedures
A preliminary meeting was held with the participants in the study, where participants were introduced to the Google Classroom online learning environment and how to effectively use it in their vocabulary learning as part of the Writing I course. Soon after, an Arabic version of the Group Embedded Figures Test (GEFT) standardised by Al-Sharkawi (Version V, 2013) was administered to all participants to classify them into context-dependent and context-independent learners. The EVRRT and the IMAS were administered to the two research groups after identifying the participants into context-independent (N = 61) and context-dependent (58) learners before the intervention. Vocabulary teaching and learning via Google Classroom was conducted for eight weeks. Then, the EVRRT and IMAS the items of which were presented to the participants in the research groups in random order for post-testing.
Vocabulary retention
Having administered the EVRRT to participants in both groups, independent t-tests for the pre-test, post-test and delayed test, respectively (Table 1 & Table 2). Pre-tests on the EVRRT indicated that the two groups were equivalent and homogeneous. The post-test results indicated a statistically significant difference between the two groups' pre-test and post-vocabulary test scores.
| Test | Group | N | Mean | Mean difference | Std. Deviation | t-value | Sig. (2-tailed) |
|---|---|---|---|---|---|---|---|
| Vocabulary Pre-test | Ex G1 | 60 | 24.183 | 3.2599 | .254 | .800 | |
| Ex G2 | 57 | 24.035 | 3.0412 | ||||
| Vocabulary Post-test | Ex G1 | 60 | 43.717 | 5.57 | 2.0838 | 12.494 | .000 |
| Ex G2 | 57 | 38.140 | 2.6889 |
By extrapolating the results from Table 2, it is clear that there are no statistically significant differences between the mean scores of the experimental group with context-independent cognitive styles and the experimental group with context-dependent cognitive styles (t = .254, p ≤ 0.05) on pre-testing achievement in vocabulary retention as assessed by the EVVRT.
However, on post-testing of vocabulary retention, there are statistically significant differences between the mean scores of the experimental group with context-independent cognitive styles and the experimental group with context-dependent cognitive styles (t = 12.494, p ≤ 0.05) in favour of the context-independent cognitive styles group. This result disconfirms the first hypothesis that there is no statistically significant difference (p ≤ 0.05) between the mean scores of the experimental group with the context-independent cognitive styles and the experimental group with the context-dependent cognitive styles on post-testing of the language vocabulary achievement test. A mean difference of 5.57 was detected between both groups on the post-EVRRT to the good of the cognitively context-independent group.
| Test | Group | N | Mean | Mean difference | Std. Deviation | t-value | Sig. (2-tailed) |
|---|---|---|---|---|---|---|---|
| Motivation Pre-test | Ex G1 | 57 | 183.78 | 52.04 | 2.49 | .015 | |
| Ex G2 | 60 | 215.72 | 56.71 | ||||
| Motivation Posttest | Ex G1 | 57 | 199.28 | -44.27 | 44.39 | 3.69 | .000 |
| Ex G2 | 60 | 155.00 | 56.59 |
Motivation
Using the IMAS, statistically significant differences between the mean scores of the experimental group with context-independent cognitive styles and the experimental group with context-dependent cognitive styles (t = 2.49, p < 0.05) were detected to the good of the dependent-context cognitive styles group. However, on post-testing instrumental motivation for both groups, statistically significant differences between the mean scores of the experimental group with context-independent cognitive styles and the experimental group with context-dependent cognitive styles (t = 3.69, p < 0.05) were detected to the good of the independent-context cognitive styles group. This result disconfirms the hypothesis that there is no statistically significant difference between mean scores of the experimental group with the context-independent cognitive style and the experimental group with the context-dependent cognitive style on post-testing of the instrumental motivation scale. A mean difference was detected between the post-test mean scores in both groups of about − 44.27 to the good of the cognitively context-independent group.
Correlation between vocabulary retention and instrumental motivation
A Pearson's Correlation Coefficient was computed for overall vocabulary retention motivation overall mean responses, shown in Table 3 below:
| Group 1 Vocabulary Retention | Group 2 Vocabulary Retention | Post-motivation Group 1 | Post-motivation Group 2 | ||
|---|---|---|---|---|---|
| Group 1 Vocabulary Retention | Pearson Correlation | 1 | 0.07 | 0.01 | -0.05 |
| Sig. (2-tailed) | 0.59 | 0.94 | 0.77 | ||
| N | 60 | 57 | 36 | 36 | |
| Group 2 Vocabulary Retention | Pearson Correlation | 0.07 | 1 | -0.22 | -0.16 |
| Sig. (2-tailed) | 0.59 | 0.19 | 0.36 | ||
| N | 57 | 57 | 36 | 36 | |
| Group 1 Post-motivation | Pearson Correlation | 0.01 | -0.22 | 1 | .43** |
| Sig. (2-tailed) | 0.94 | 0.19 | 0.01 | ||
| N | 36 | 36 | 36 | 36 | |
| Group 2 Post-motivation | Pearson Correlation | -0.01 | -0.16 | .430** | 1 |
| Sig. (2-tailed) | 0.77 | 0.36 | 0.01 | ||
| N | 36 | 36 | 36 | 36 | |
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
As Table 3 demonstrates, there is no correlation between vocabulary retention as a function of using the Google Classroom e-learning environment and motivation mean responses on post-testing. However, running another detailed Pearson's correlation coefficient between the vocabulary retention test results and the post-motivation mean responses for each dimension of the IMAS revealed some correlation between some of the dimensions of the IMAS and the overall mean scores of the students on the EVERRT, thus validating the hypothesis that there is no correlation between developing English vocabulary and instrumental motivation, as shown in Table 4 below:
For the first control group, the participants with independent cognitive styles, there is a statistically significant correlation between competitiveness and vocabulary retention on the one hand (R = 0.94) and self-confidence and vocabulary retention on the other (R = 0.93) at the 0.01 level (2-tailed). Furthermore, a statistically significant correlation exists between rewardedness and vocabulary retention in the first group with independent cognitive styles at the 0.05 level (2-tailed). However, there are no correlations between post-motivation components and vocabulary retention in the second group, with participants classified as cognitively context-dependent.
This study's findings showed that participants with cognitive context-independent styles outperformed their peers with context-dependent styles in their vocabulary retention performance on the EVRRT. This result confirms prior research findings that learning styles are environmentally sensitive (Cools & Rayner, 2011; Evans & Cools, 2011; Evans & Waring, 2013; Kozhevnikov et al., 2014; Peterson, Rayner, & Armstrong, 2009). The results add to the accumulated evidence that a particular learning environment could influence the poles of dependency versus independency of context as the locus of control in context-independent individuals is internal as they prefer to learn through written or oral verbal language and achieve better on vocabulary retention tests that require perception and understanding of details - a characteristic of vocabulary learning. However, their context-dependent peers tend to think holistically, linearly and through full awareness of the learning material, thus tending to achieve lower on tests that rely on memorisation and understanding, as such individuals fail to deal with too much information (Kommers et al., 2008; Menaker & Coleman, 2007). In addition, using an e-learning environment such as Google Classroom with its discussion forums and peer work activities is consistent with the constructivist approach and the cognitive styles of context-independency who prefer to work on details which characteristically describe how vocabulary is learned and retained; exchanges between cognitively context-independent participants in the area of vocabulary learning could help students engage in higher-order information processing and construct their meanings - findings that are consistent with extant research (Kommers et al., 2008; Kozhevnikov et al., 2014; Küçük et al., 2010; Menaker & Coleman, 2007).
The improvements in vocabulary retention could also be ascribed to the use of the facilities in the Google Classroom, which contains graphics, images, videos and other mnemonics which could have helped the context-independent cognitive styles group, meshing with prior research findings in this vein, too (Chen & Chung, 2008; Ebbihaus, 1913; Driscoll, 2005). Learning by online learning environments that capitalise on multimodal presentation and spaced repetition to help students successfully learn vocabulary, retrieve it and retain it when needed (Kohnke, 2019) since the inclusion of multimedia induces lexical understanding and retrieval (See, for instance, Mayer, 2009; Wang et al., 2020).
Regardless of the slight improvements in vocabulary retention in cognitively context-dependent participants, compared with their context-independent students, on post-testing, the latter group were better able to use effective retention strategies. At the same time, the former was involved in self-dependent and holistic vocabulary learning, causing them to encounter retention problems and frustration or lose motivation for learning (See, for example, Dörnyei & Csizér, 2002).
Considering motivation, where learning is not a goal per se, it was revealed that context-dependent participants were more motivated initially than their context-independent peers on pre-tests of the IMAS. However, later after the intervention in the Google Classroom, their cognitively context-independent peers outperformed them on instrumental motivation on post-testing. Improvements in motivation could be detected as a developing construct when learners could adapt to the e-learning environment they were engaged in and perform better on exams, as was reported in some prior research (e.g., Lens et al., 2009; Nizam et al., 2021; Ryan & Deci, 2000).
Improvements in vocabulary retention and instrumental motivation could be explained by flexible learning that provided an opportunity for students to review the learning material more than once; this could have possible contributed to increased cognitive performance, regardless of participants' cognitive style. However, it was far better for context-independent students. In addition, the diversity in the forms of educational content provided to students through Google Classroom contributed to considering their differences in cognitive styles for the good of context-independent students. Furthermore, reliance on constructivist teaching and learning in presenting the learning materials via Google Classroom contributed to helping students process and store vocabulary in a way that is easy to recall and deal with when needed. For example, activating the discussion forum contributed to the achievement of asynchronous interaction between students and each other and the students and the professor of the subject. Also, students' enthusiasm and motivation for learning in this way contributed to more effort and more students' involvement in the educational process and the transformation of their role into a positive and active role, which contributed to further progress.
Based on the results of the study, the researchers recommend that EFL teachers' attention be redirected to the use of electronic environments for teaching and learning, given the positive impact on the outcomes of the educational process. In addition, those in charge of the educational process should be directed to redesigning and improving learning environments to match the varied learning styles of EFL students, and in this way, too, testing and assessment should be developed to appropriate students’ different learning styles. Linking the curriculum of foreign languages with the requirements of the educational environment will engulf individual differences among students so that EFL teachers can present the material in manners commensurate with the characteristics and cognitive styles of each student. Therefore, professional development courses for teachers should be developed and offered to foreign language teachers so they could be fully aware of all developments that can be invested in the educational process. The findings of the present study suggest that more studies on e-learning environments and their impact on other dependent variables should be conducted on other aspects and skills of learning foreign languages. Finally, further research dealing with the interaction between electronic learning environments and other types of cognitive styles should be conducted to investigate achievement in other language skills and explore effects on other affective variables.
The students who participated in this study gave their consent to take part in this study provided that they be deidentified, and that the data be only used for research purposes only. Participants consented that the results of our study may be published, still with their personal data removed."
The authors of this manuscript preprint declare no competing interests.
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The Appendices is not available with this version