A Serial Mediation Approach to Goal Orientations,Learning Strategies and Achievement Outcomes on a Computer-Mediated English Program

Abstract

Computer-mediated learning initiatives have recently increased due to the novel coronavirus pandemic. Implications are thus created for self-regulation, learning and achievement as computer-mediated learners face unique motivational and metacognitive demands. The current research uses a serial mediation approach to test the effect of goal orientations and the mediatory role of learning strategies on achievement among 195 Japanese university students on a 24-month computer-mediated English program. The tested model shows a good fit to the data and accounts for 28.5% of the achievement variance. Direct effects are observed between a performance-approach orientation and achievement. Mediation effects are also observed between a performance-approach orientation and achievement via a strategic approach to learning. In contrast, the effect of a mastery orientation on achievement is mediated via a strategic approach to learning. A mediated serial effect is also observed between a mastery orientation and achievement via a deep and strategic approach to learning. The challenges facing teachers and students of computer-mediated learning solutions in English as a foreign language (EFL) education and the importance of providing an informed experience that facilitates, supports and rewards adaptive motivations and approaches to learning are discussed.

Keywords

approaches to learning computer-mediated education English as foreign language goal orientations mediation

Introduction

Computer-mediated learning solutions are a prevalent feature of higher education and the broader transnational learning experience. Further opportunities have recently arisen in response to the novel coronavirus pandemic, an unprecedented occurrence facilitating the “largest disruption of education systems in history, affecting nearly 1.6 billion learners in more than 190 countries and all continents” (United Nations, 2020, p. 2). For several decades, computer-assisted language learning (CALL) practitioners have striven to integrate computers into the language education curriculum. While best practice might award equal importance to considerations of pedagogy, theory and technology (Garrett, 2009), English as a foreign language (EFL) education has been sluggish to establish exactly how the computer informs the acquisition of language (Golonka et al., 2014). CALL research deriving from an EFL perspective has often been restricted to a technocentric focus, and in doing so, has sidelined considerations of pedagogy and theory (Bodnar et al., 2016; Gillespie, 2020; Hubbard, 2009). Given the shift toward the greater use of computer-mediated solutions, it is imperative that research attention is directed toward the processes of learning and their effect on achievement outcomes. As an integral component of self-regulation (Wolters et al., 1996), motivation provides a platform from which to undertake such research.

Significance of Research

The relationship between motivation and achievement is often conceptualized within educational psychology through a goal orientation framework (Diseth, 2011; Elliot et al., 1999; Murayama & Elliot, 2009; Steinmayr et al., 2011). Prior research into the effect of goal orientations on achievement has been inconclusive and often reliant on data drawn from the traditional classroom (Cho & Heron, 2015; Kramarski & Mizrachi, 2006; Mouratidis et al., 2013; Payne et al., 2007; Pintrich, 2000; Pintrich & Schunck, 2002; Senko et al., 2011; Shea & Bidjerano, 2010). On the premise that goal orientations cannot fully inform achievement unless expressed through an actionable approach to learning (Elliot & Harackiewicz, 1996; Pintrich & De Groot, 1990), the relationship between motivation and achievement is ideally tested via mediation. Drawing from a goal orientation framework, and positioning approaches to learning as a mediator, the current study assesses how learner motivations effect achievement on a 24-month computer-mediated English program administered at a Japanese university by three English-speaking CALL researchers. Two research questions are used to structure the study.

RQ1: Does motivation within a goal orientation framework have a direct effect on achievement on a computer-mediated English program?

RQ2: Does the approach to learning taken mediate the relationship between motivation and achievement on a computer-mediated English program?

A brief theoretical background is first presented in relation to the formal hypotheses to be tested. The hypothesized model of interaction is then shown and the methods used are described. Following the presentation of the results, the outcomes are discussed in relation to the challenges facing teachers and students of computer-mediated learning solutions in EFL education and the importance of providing an informed experience that facilitates, supports and rewards adaptive motivations and approaches to learning are discussed.

Theoretical Background and Hypotheses

Goal Orientations

Motivation reflects an internal state of arousal pertaining to a stimulus. Within educational research it has been studied as a series of hierarchical expectancy-values (Ames, 1992). Achievement motivation through a goal orientation framework concerns the way in which individuals conceive of themselves, their learning activities and their academic performances (Ames & Archer, 1988; Anderman & Anderman, 1999; Anderman & Midgley, 1997). Divergent goal orientations guide individuals toward specific outcomes. Goal orientation theorists suggest that the orientation adopted at the outset of a learning initiative creates a cognitive structure for interpretation, evaluation and action (Elliot & Harackiewicz, 1996). Early goal orientation research identified a desire for success and a desire to avoid failure as critical determinants of aspirational behavior (Kukla, 1972; Stein & Bailey, 1973). Contemporary goal orientation research has retained the distinction between a desire for success and a desire to avoid failure while often inclusive of a third variable (Deci & Ryan, 1985; Nicholls, 1984). Ames and Archer (1988) have stated that the third variable identified within many studies warrants convergence into what is now known as a mastery orientation. The current trichotomous goal orientation framework therefore includes a mastery orientation, a performance-approach orientation and a performance-avoidance orientation (Elliot & Church, 1997).

A mastery orientation reflects “a preference for moderately challenging tasks, persistence in the face of failure, a positive affective stance toward learning, and enhanced task enjoyment” (Elliot & Harackiewicz, 1996, p. 462). Learning under this impetus generates excitement and other affective emotions (Harackiewicz et al., 1998) while the potential reward of increased understanding promotes investment, dedication and perseverance (Kaplan et al., 2002). These motives are informed by self-regulatory processes relative to positive achievement outcomes and the attainment of normative competence. Progress is measured in self-referential rather than comparative terms (Midgley et al., 1998). In theory, a mastery orientation is generally seen as adaptive in relation to learning. Midgley et al. (2001) draw attention to the consistent relationship between mastery and adaptive patterns of cognition and behavior such as an interest in learning materials, persistence and self-regulation (Ryan & Pintrich, 1997). In practice, research linking mastery with achievement is inconsistent (Phillips & Gully, 1997). Senko and Miles (2008) argue that learners who commit to a mastery orientation seldom perform better than students with other motivations. The authors speculate that learners seeking a deeper understanding of materials are, in doing so, hindering their own chances of achievement. It remains that only a small number of studies have been able to demonstrate a direct link between a mastery orientation and achievement, particularly when performance orientations are examined together as concurrent predictors (Elliot & Murayama, 2008; Pekrun et al., 2009).

In terms of promoting achievement a combination of mastery and performance-approach orientations are ideal within competitive courses which grade students on a curve (Harackiewicz et al., 2002). Performance orientations are externally referenced in relation to others meaning that self-regulation from a performance orientation perspective corresponds to either a desire for positive appraisal in relation to peer-based normative competence or a desire to avoid negative appraisal in relation to the same criteria. Despite a performance-approach orientation reflecting an external motivation, many studies have framed it is as an adaptive approach to learning, but only within certain learning environments (Pajares et al., 2000) such as those competitively structured (Darnon et al., 2009; Harackiewicz et al., 1998) or dominated by male learners (Bouffard et al., 1995). In contrast, the performance-avoidance orientation is almost universally framed as maladaptive as it draws impetus from esteem-related fear, anxiety and avoidance, thus showing a dominant concern with the psychological protection of self-worth through maladaptive action (Covington, 1992). Motivated through a desire to protect oneself from an exposed lack of competence in relation to others, such individuals are prone to process the learning environment as threatening and uncomfortable. This situational processing promotes absenteeism, non-participation and withdrawal (Elliot & Thrash, 2001).

H1: Mastery has no direct effect on achievement

H2: Performance-approach has a direct positive effect on achievement

H3: Performance-avoidance has a direct negative effect on achievement

While the distinction between a performance-approach and a performance-avoidance orientation indicates one as being more adaptive than the other, it has been suggested that both performance orientations are disruptive due to their externally referenced sources of motivation (Midgley et al., 2001; Rawsthorne & Elliot, 1999). Links have also been made between performance orientations and academic dishonesty. Sideridis and Stamovlasis (2014) outline that “when challenged in an achievement situation, the elements of self-worth preservation and self-depreciation may direct an individual to engage in academic cheating in order to protect their self-worth” (pp. 446–447). The authors further identify performance related motivations as the most significant predictors of academic cheating and dishonesty.

Approaches to Learning

Deriving from the identification of deep and surface processing (Marton & Saljo, 1976), approaches to learning research is a central theme in educational discourse. The elemental distinction drawn out between the two levels of processing asserts that learners who adopt surface processing aim to remember and recall information as a means of achievement, whereas those who adopt deep processing seek to understand the intent of the information as a pathway to increased comprehension. The approach taken by the learner is influenced by several factors including context-fixed definitions and expectations of achievement in addition to individual definitions of achievement. Approaches to learning research considers the composition of contextualized learning activities as likely to influence the relational approach taken by the learner (Evans et al., 2003; Fenollar et al., 2007). The relative assessment indicators of each approach therefore differ in relation to context although the fundamental processing orientation underpinning the differentiations remains constant.

A deep approach to learning is reflected in behaviors that seek meaning within the subject matter and attempt to critically relate it to other experiences and ideas. The use of evidence and the linking together of ideas are dominant indicators of a deep approach to learning (Diseth & Martinsen, 2003). Learners who adopt a deep approach to learning have an intrinsic interest in the subject matter which promotes enjoyment and satisfaction. A deep approach to learning is often considered essential to the development of comprehension (Pintrich & De Groot, 1990; Pressley & McCormick, 1995). Other studies have found that while a mastery orientation is often predictive of a deep approach to learning (primed by intrinsic factors) and a performance-approach orientation is often predictive of a surface approach to learning (primed by external factors), the relationship is dependent on context (Pekrun & Stephens, 2009; Vrugt & Oort, 2008).

H4: Mastery has a direct positive effect on deep approach

H5: Performance-approach has a direct negative effect on deep approach

H6: Performance-avoidance has a direct negative effect on deep approach

Learners who adopt a surface approach to learning tend to view the subject matter and related activities as an imposition. These learners isolate individual parts of the subject and frame them as delineated from one another. This approach is characterized by a failure to integrate ideas and perspectives into a coherent overall learning agenda. A surface approach to learning primarily draws from a fear of failure and manifests as a reliance on techniques such as rote-learning and memorization (Diseth & Martinsen, 2003; Elliot et al., 1999; Entwistle, 1988). Wolters (2004) further highlights how assessment methods which include “completing repetitive work, being compliant, well-behaved, or generally exhibiting a surface level understanding of the material” (p. 248) cannot be expected to facilitate or reward the adoption of a deep approach to learning. In contrast, Harackiewicz et al. (1998) point out that a surface approach to learning “may actually be quite effective in preparing for multiple choice exams in introductory classes” (p. 1292). The authors explain that if higher education examinations are not intended to test and reward a deep level of cognitive processing through the considered synthesis of knowledge and information, then mastery orientated learners will be outperformed by performance orientated learners.

H7: Mastery has a direct negative effect on surface approach

H8: Performance-approach has a direct negative effect on surface approach

H9: Performance-avoidance has a direct positive effect on surface approach

A third approach, referred to as a strategic approach to learning (Entwistle & Ramsden, 1983), is more sensitive to the specific demands made within individual learning environments. Strategic learners are motivated by achievement underpinned by competition with others for desirable outcomes. These learners act upon this desire through contextually informed calculations relating to effective time management, organized study methods and awareness of the assessment demands in alignment to their own intellectual abilities (Duff, 2003).

H10: Mastery has a direct positive effect on strategic approach

H11: Performance-approach has a direct positive effect on strategic approach

H12: Performance-avoidance has a direct negative effect on strategic approach

A strategic approach is not applicable to all students as those who believe that the meaning of learning is apparent (e.g. to acquire knowledge and understanding for the benefit of the self) will adopt this perspective across all learning situations regardless of content differences. A strategic approach to learning is thus more malleable than a deep or surface approach in that it does not relate to a fixed conceptualization of learning. Learners instead deploy whichever strategic actions they believe to be beneficial to achievement in context (Diseth & Martinsen, 2003). A strategic approach to learning may therefore combine elements of a deep and surface approach as the context demands, although the motivational intent relates to achievement rather than an intrinsic interest in learning (deep) or a fear of failure (surface).

H13: Deep approach has a direct positive effect on strategic approach

H14: Surface approach has a direct negative effect on strategic approach

H15: Deep approach has a direct positive effect on achievement

H16: Strategic approach has a direct positive effect on achievement

H17: Surface approach has a direct negative effect on achievement

Several studies have examined the mediated relationship between goal orientations, approaches to learning and achievement (Boekaerts & Niemivirta, 2000; Nolen, 1988, 1996; Pintrich, 2000; Weinstein et al., 2000). Elliot et al. (1999) identify a positive relationship between a performance-approach orientation and exam grade when mediated by persistence. The authors also observed a negative relationship between a performance-avoidance orientation and achievement when mediated by a surface approach to learning in the form of disorganization. A negative relationship between a performance-avoidance orientation and exam grade when mediated by a surface approach to learning in the form of test anxiety has also been reported (Elliot & McGregor, 1999).

H18: Deep approach positively mediates goal orientation and achievement

H19: Strategic approach positively mediates goal orientation and achievement

H20: Surface approach negatively mediates goal orientation and achievement

Greene et al. (2004) document that learning strategies mediate the relationship between a mastery orientation and achievement, while Magen-Nagar and Cohen (2017) contend that learning strategies mediate the relationship between motivation and a sense of achievement. Fenollar et al. (2007) report that while goal orientations have no direct effect on academic performance, a deep approach to learning mediates the relationship between mastery and academic performance. Finally, Diseth and Kobbeltvedt (2010) contend that strategic approaches to learning mediate the relationship between goal orientations and academic achievement.

The Research Model

As outlined in previous sections, research indicates that while goal orientations may exert a direct effect on achievement, it is more logical to assess their influence as mediated through an actionable approach to learning (Elliot & Harackiewicz, 1996; Pintrich & De Groot, 1990). A hierarchical relationship between the variables is therefore proposed. Figure 1 outlines the hypothesized pattern of effects between the latent variables in the current study.

Figure 1.

The Hypothesized Serial Mediation Model to be Tested (the H1 Path is not Shown Due to Practical Layout Considerations).

Methods

The Computer-Mediated English Program

The computer-mediated English program is situated within a Japanese university. The 24-month program (assessment points at 6, 12, 18 and 24 months) is communicated to learners through a Japanese language handbook via the Moodle learning management system.¹ The handbook is created by three English-speaking CALL researchers who also administer the program. All three CALL researchers are qualified to the master’s level of education. The handbook defines the program as a compulsory online subject intended to maintain and improve the English language proficiency of learners (あなたの英語力の維持・向上を支援する目的でデザインされた、オンラインの必修科目です). Learners are told that all work is done online through the internet (作業のすべてをインターット上、つまりオンラインで行います) although in reality several of the activities are undertaken face-to-face. While the program claims that participants can learn English at their own convenience (自分の都合のよい時間に、好きな場所で英語を学ぶことができます) meaning that they must make their own study time as there is no one to manage their schedule (自分で [program name]の学習時間を作る必要があります。さらに、自分で立てたスジュールを管理してくれる人もいないので、自発的に [program name] コースにアクセスする必要があります), the convenience permitted to students is tapered by a fixed schedule which includes start dates and deadlines for all of the activities (スちジュールには、[program name] タスクすてべての開始日と締め切りが記されています) (see Figure 2).

Figure 2.

A Schedule Given to Learners Showing the Range of Activities and the Deadlines.

Despite its long-standing status within the university, the reliability and predictive validity of the program have not been established through empirical research or published data. Throughout the handbook, it is not clear how many of the activities relate to English proficiency development nor how the content of certain activities, especially those which are unstructured or graded based on attendance, informs and educates in relation to formal English language acquisition. In fact, at no point within the handbook are learners told how each of the activities, computer-mediated or otherwise, can improve their English proficiency nor how the content of the activity is monitored, regulated or graded in relation to a certain proficiency level expectation or competency outcomes. The program therefore prioritizes information relevant to instructor expectations of task completion rather than expectations relating to the isolated development of specific learner-centered competencies and proficiency. The computer-mediated English program serves as an example of the technocratic focus commonplace within CALL and EFL research in which pedagogical and theoretical concerns are marginalized or overlooked.

Participants and Procedure

Participants were male (n = 155) and female (n = 40) learners enrolled in the 24-month computer-mediated English program. After 12-months of the program all enrolled learners were sent an email invitation by the author with a URL link to an online Japanese language survey measure. The survey had first been constructed in English before being translated into Japanese by an independently contracted translator. The external translation was then verified by a Japanese-speaking colleague. All survey statements were assessed using a five-point Likert-type scale ranging from “very much disagree” to “very much agree”. Consenting participants were given a descriptive overview of the research and were requested to complete the survey within a 14-day period. Participants ranged in age from 18–23 years (mean = 18.96; SD = .82). Survey items were automatically randomized for each respondent to avoid order effects bias.

Measures and Analysis

The aim was to identify reliable measurement models using three or four items per latent variable (Kenny, 1979) (see Appendix). Construct reliability (Taber, 2018), convergent validity (Dillon & Goldstein, 1984) and discriminant validity (Fornell & Larcker, 1981) were established for each of the measurement models. Concerning the convergent validity and reliability assessment, Fornell and Larcker (1981) suggest average variance extracted (AVE) values >.50 and composite reliability (CR) values >.70. Discriminant validity was affirmed through calculating whether the square root of the average variance extracted for each latent construct was greater than the off-diagonal loadings on the factor correlation matrix. Assessments of model fit were made using the chi-square, normed chi-square, Tucker-Lewis index (TLI), comparative fit index (CFI) and the root mean squared error of approximation (RMSEA), all of which were selected prior to the analysis.

Goal Orientations

The 18-item Goal Orientation Scale (Midgley et al., 1998) was administered. The final measurement model retained nine items with three items loading on to each of the three orientations. Performance-approach (α = .82), mastery (α = .89) and performance-avoidance (α = .86) showed acceptable construct validities with factor loadings between .68 and .91. The three-factor measurement model returned a good fit to the data [χ2 = 38.053 (df = 24), p = .034, NC = 1.586, TLI= .979, CFI = .986, RMSEA=.054].

Approaches to Learning

The 52-item Revised Approaches to Studying Inventory (RASI) (Entwistle, 1997; Entwistle & Ramsden, 1983) was administered. This measure assesses learning strategy preference via several sub-scales. On the original scale a deep approach to learning is inclusive of scales pertaining to seeking meaning, relating ideas, use of evidence, interest in ideas and monitoring effectiveness. A strategic approach to learning is inclusive of scales pertaining to organized studying, time management, achieving and alertness to assessment demands. A surface approach to learning is inclusive of scales pertaining to a lack of purpose, unrelated memorizing, fear of failure and syllabus-boundness. However, previous studies have reported weak construct validities even when the sub-scales are parceled into single composite factors. Eight items were therefore selected to reflect a deep approach to learning from the seeking meaning and related ideas sub-scales. Similarly, eight items were selected to reflect a strategic approach to learning from the organized studying and time management sub-scales. Eight further items were also selected to reflect a surface approach to learning from the unrelated memorizing and fear of failure sub-scales. The final measurement model retained nine items with three items loading on to each approach. Deep (α = .74), strategic (α = .77) and surface (α = .73) approaches to learning showed acceptable construct validities with factor loadings between .64 and .79. The average variance extracted values for the deep (.49) and surface (.48) approaches were below the recommended cutoff of >.50. However, composite reliability values > .70 are sufficient to indicate “that the convergent validity of the construct is adequate, even though more than 50% of the variance is due to error” (Fornell & Larcker, 1981, p. 46). The three-factor measurement model returned an acceptable fit to the data [χ2 = 48.586 (df = 24), p=.002, NC = 2.024, TLI= .923, CFI = .948, RMSEA=.072].

Achievement

The composite assessment grades awarded to learners at four intervals during the computer-mediated English program (6,12,18 and 24 months) were used to reflect achievement. As the composition of each respective time point grade was not standardized across the course or within the actual population of learners, other methods of analysis such as latent variable growth curve modeling were deemed inappropriate. The achievement factor (α = .81) showed acceptable construct reliabilities with factor loadings between .51 and .97. The single-factor measurement model returned a good fit to the data [χ2 = 2.146 (df = 1), p=.143, NC = 2.146, TLI= .979, CFI = .997, RMSEA=.077].

Table 1 shows that achievement is significantly correlated with the goal orientation and approaches to learning variables providing rationale for testing the hypothesized interactions shown in Figure 1.

Table 1.

Reliability and Validity Statistics.

Construct	AVE	CR	1	2	3	4	5	6	7
1. Performance-Approach	.64	.84	.80
2. Mastery	.73	.89	.24**	.85
3. Performance-Avoidance	.68	.86	.53**	.28**	.82
4. Deep	.49	.74	.15*	.29**	.17*	.70
5. Strategic	.54	.77	.29**	.35**	.28**	.35**	.73
6. Surface	.48	.74	−.31**	−.06	−.23**	−.28**	−.12	.69
7. Achievement	.53	.81	.35**	.22**	.14*	.17*	.34**	−.14*	.73

Italicized values represent discriminant validity.

Table 2.

Direct Effects and Hypotheses Testing.

Hyp	Direct effects		Std. β	SE	95% CI		Hyp Support
Hyp	Direct effects		Std. β	SE	Lower	Upper	Hyp Support
H1	Mastery	→ Achievement	.030	.101	−.165	.223	Yes
H2	Performance-Approach	→ Achievement	.429**	.115	.182	.642	Yes
H3	Performance-Avoidance	→ Achievement	−.050	.135	−.299	.224	No
H4	Mastery	→ Deep	.041	.093	−.134	.212	No
H5	Performance-Approach	→ Deep	.083	.141	−.195	.371	No
H6	Performance-Avoidance	→ Deep	.041	.093	−.134	.212	No
H7	Mastery	→ Surface	.201	.111	−.006	.430	No
H8	Performance-Approach	→ Surface	−.394**	.122	−.646	−.170	Yes
H9	Performance-Avoidance	→ Surface	.062	.144	−.226	.357	No
H10	Mastery	→ Strategic	.326**	.107	.097	.520	Yes
H11	Performance-Approach	→ Strategic	.256	.146	−.041	.533	No
H12	Performance-Avoidance	→ Strategic	.055	.134	−.217	.307	No
H13	Deep	→ Strategic	.321*	.123	.050	.536	Yes
H14	Surface	→ Strategic	.111	.143	−.183	.380	No
H15	Deep	→ Achievement	−.085	.113	−.301	.132	No
H16	Strategic	→ Achievement	.327**	.093	.137	.498	Yes
H17	Surface	→ Achievement	.018	.126	−.260	.245	No

Table 3.

Specific Indirect Effects, Serial Mediation Effects and Hypotheses Testing.

Hyp	Indirect, Serial and Effects			Std. β	SE	95% CI		Hyp Support
Hyp	Indirect, Serial and Effects			Std. β	SE	Upper	Lower	Hyp Support
H18a	Mastery	→ Deep	→ Achievement	−.238	.391	−1.195	.337	No
H18b	Mastery	→ Deep → Strategic	→ Achievement	.295*	.231	.043	.895	Yes
H18c	Performance-Approach	→ Deep	→ Achievement	−.060	.202	−.891	.130	No
H18d	Performance-Approach	→ Deep → Strategic	→ Achievement	.074	.167	−.148	.568	No
H18e	Performance-Avoidance	→ Deep	→ Achievement	−.040	.176	−.738	.145	No
H18f	Performance-Avoidance	→ Deep → Strategic	→ Achievement	.050	.152	−.169	.458	No
H19a	Mastery	→ Strategic	→ Achievement	.564*	.358	.022	1.553	Yes
H19b	Performance-Approach	→ Strategic	→ Achievement	.718*	.450	.025	1.936	Yes
H19c	Performance-Avoidance	→ Strategic	→ Achievement	.176	.462	−.594	1.269	No
H20a	Mastery	→ Surface	→ Achievement	.005	.124	−.205	.383	No
H20b	Mastery	→ Surface → Strategic	→ Achievement	.009	.058	−.061	.240	No
H20c	Performance-Approach	→ Surface	→ Achievement	−.062	.472	−1.057	.775	No
H20d	Performance-Approach	→ Surface → Strategic	→ Achievement	−.123	.192	−.684	.149	No
H20e	Performance-Avoidance	→ Surface	→ Achievement	−.008	.162	−.481	.230	No
H20f	Performance-Avoidance	→ Surface → Strategic	→ Achievement	−.016	.079	.082	.491	No

Results

SPSS AMOS 23 was used to test the full-structural model. The full-structural model is inclusive of standardized regression paths (β) with the level of significance showing the strength of the relationship between independent and dependent variables. The model also shows r-squared values in the endogenous variables which represent the shared variance accounted for by the effect of the independent variables.

SPSS AMOS 23 does not provide calculations for specific (rather than summative) indirect effects. User-defined estimands were therefore written using visual basic coding. Bootstrapping (1000 samples, 95% CI) with bias-corrected confidence estimates were used (MacKinnon et al., 2004; Preacher & Hayes, 2008). This approach was selected over the causal step approach (Baron & Kenny, 1986) as the bootstrapping resampling procedure not only tests the mediation hypotheses but also retains greater statistical power (Williams & MacKinnon, 2008). Figure 3 shows the full-structural model tested which returned a good fit to the data [χ2 = 254.234 (df = 187) p = 0.001, NC = 1.360, TLI = 0.957, CFI= 0.965, RMSEA= 0.043]. Moreover, Table 2 and Table 3 detail the specific direct and indirect effects in relation to the hypotheses tested. The model accounted for 28.5% of the shared variance in achievement. While not included in the formal hypotheses, significant correlations were observed between the three goals orientations suggesting that they are not mutually exclusive.

Figure 3.

The Full Structural Model Tested (a Full Line Denotes Significant Direct Effects and a Broken Line Denotes Significant Indirect Effects). *p < 0.05, **p < 0.01, ***p < 0.001.

Discussion

The current research has tested a hypothesized model of interactions examining the direct and mediated relationship between goal orientations, approaches to learning and achievement on a 24-month computer-mediated English program administered at a Japanese university by three native English-speaking CALL researchers. The two research questions previously identified can now be discussed in relation to the tested model.

RQ1: Does motivation within a goal orientation framework have a direct effect on achievement on a computer-mediated English program?

A mastery orientation did not have a direct effect on achievement (accept H1) which reflects prior studies inclusive of the two other goal orientations as concurrent outcome predictors (see Elliot & Murayama, 2008; Pekrun et al., 2009). Only the performance-approach orientation (β = .429, p < 0.01) had a direct effect on achievement (accept H2, reject H3). The computer-mediated learning environment is therefore established in the self-regulatory framework of the learner as primarily concerned with a competition for limited achievement resources. This reflects prior research in which the performance-approach orientation is adaptive in terms of its effect on achievement within competitive environments and those dominated by male learners (see Bouffard et al., 1995; Darnon et al., 2009; Harackiewicz et al., 1998). An implication of this relates to suggestions that competitive peer-referenced motivations are ultimately destructive in the longer term (Midgley et al., 2001; Rawsthorne & Elliot, 1999). Learners who adopt a performance-approach orientation are prone to hold a performance-avoidance orientation at some point (note the significant positive correlation between these two orientations). This caution is particularly applicable to learners with lower levels of English proficiency in order to avoid promoting helplessness via repeated unsuccessful outcomes generated through peer-referenced comparison. This caution is also applicable to learners with high academic self-efficacy who are at risk from becoming performance-avoidance orientated once the performance-approach motivation ceases to provide satisfactory peer-referenced feedback (Brophy, 2005; Middleton et al., 1998).

It has been suggested that competitive students will engage in maladaptive practices such as cheating and collaborative deception once peer-referenced feedback is unfavorable (Sideridis & Stamovlasis, 2014). Such behaviors have been uncovered among the enrolled students. As a result the current computer-mediated English program requires all students to agree to an honor code. Whether cheating and deception relate to an unsatisfied competitive drive among high academic self-efficacy learners or whether it serves to compensate weaker students for a lack of actual competence and proficiency is unknown (see Curtis & Popal, 2011). The compulsory credit bearing nature of the program can nonetheless be expected to exemplify both possibilities, particularly in the absence of other more adaptive intrinsic motivations. In other words, while most learners do complete the activities assigned within the environment primed in relation to instructor expectations of task completion, their motivation for doing so, and thus the anticipated reward, may cease to extend beyond the formal attainment of the required credit.

RQ2: Does the approach to learning taken mediate the relationship between motivation and achievement on computer-mediated English program?

A mastery and performance-approach orientation had positive mediated effect on achievement. A mastery orientation influenced achievement when serially mediated through a deep and strategic approach to learning (β = .295, p < 0.05) (accept H18b) and when mediated through the strategic approach to learning (β = .564, p < 0.05) (accept H19a). Although there was a direct path between a mastery orientation and a strategic approach to learning (β = .326, p < 0.01) (accept H10), there was no direct path established between a mastery orientation and a deep approach to learning (reject H4). A mastery orientation also had no relationship with a surface approach to learning (reject H7). A performance-approach orientation had a mediated effect on achievement through a strategic approach to learning (β = .718, p < 0.05) (accept H19b). This is interesting as no direct path was established between a performance-approach orientation and a strategic approach to learning (reject H11). While a performance-approach orientation had a negative effect on a surface approach to learning (β = −.394, p < 0.01) (accept H8) it did not have an effect on a deep approach to learning (reject H5). A performance-avoidance orientation had no direct effect on either a deep, strategic or surface approach to learning (reject H6, H9 and H12).

The absence of a relationship between a performance-avoidance orientation and the other variables can be considered in relation to two speculations. First, the outcome indicates that the enrolled learners are motivated by the externally referenced performance-approach orientation, and to a lesser extent, the self-referenced mastery orientation (albeit not in a manner that directly effects achievement outcomes). Such a configuration has been proposed as ideal within competitive courses which grade students on a curve (Harackiewicz et al., 2002). In this instance, if English language proficiency is not being assessed or controlled for then one must ask what is measured and thus reflected in the grades given to learners. A second possible explanation concerns the program composition. While various permutations are available, the computer-mediated English program does not require that all learners have face-to-face interactions with either the program instructors or the other enrolled students. Situated in a computer-mediated online space it is therefore more difficult to create a cohesive imagined community (Anderson, 2006) of peers as would otherwise be expected in a traditional classroom in which nuanced interpersonal considerations may have a greater impact on learning motivations and behaviors (e.g. visible competition from peers). The absence of a relationship between a performance-avoidance orientation and the other variables (despite positive correlations between the three goal orientations) can therefore be explained relative to the absence of perceived threat in relation to incidents which might expose a lack of peer-referenced normative competence. The computer-mediated learning environment does not promote avoidance, non-participation and the psychological protection of self-worth through maladaptive action (Covington, 1992).

Although no direct effects were established with achievement by a deep approach to learning (reject H15) or a surface approach to learning (reject H17), a strategic approach to learning had a direct effect on achievement (β = .327, p < 0.01) (accept H16). Within the hierarchical approaches to learning variables, a deep approach to learning had a direct effect on a strategic approach to learning (β = .321, p < 0.01) (accept H13) whereas no such effect was observed between a surface approach to learning and a strategic approach to learning (reject H14). The primary mediator between goal orientation and achievement is thus affirmed as a strategic approach to learning (β = .564, p < 0.05) (β = .718, p < 0.05) (accept H19a, H19b and reject H18a, c-f, H19c and H20a-f). A strategic approach to learning also accounted for the greatest portion of the shared variance (31.2%) within the tested model. These findings are explainable relative to a computer-mediated learning environment primed in relation to instructor expectations of task completion (e.g. Figure 2). Combined with a lack of actual pedagogical or theoretical information relevant to expected competencies and task outcomes, learners primarily self-regulate in relation to the time management and planning demands of the fixed schedule. A successful student, defined by a higher achievement outcome, might simply be one who was able to follow instructor expectations of task completion regardless of their actual English language proficiency.

Implications for Improvements in Practice

The results have directed research attention toward the processes of learning involved in a computer-mediated English program and the role such processes have in learner achievement outcomes. While the data shows how different motivations effect achievement outcomes on the 24-month program, attention must also be directed to the status of the program in relation to the claim that a great deal of CALL research deriving from an EFL perspective has been restricted to a technocentric focus, and in doing so, has sidelined considerations of pedagogy and theory (Bodnar et al., 2016; Gillespie, 2020; Hubbard, 2009). The historical legacy of “native” English speaker recruitment practices in Japan can be used as a focal point for understanding why such programs exist unchallenged despite limited pedagogical or theoretical support for their continuation (see Houghton et al., 2018; Rivers, 2011, 2019; Rivers & Ross, 2013; Rivers & Zotzmann, 2017).

It can often be found that a lack of explicit teacher training is evident wherein higher formal qualifications and professional development initiatives are overlooked in favor of native speaker status (Rivers, 2016). A lack of teacher training has also been identified within the CALL research domain. Compton (2009) describes how “the increase in online language learning has not been matched with an increase in teacher training for language teachers beyond the technical and software-specific skills” further adding that “a common approach to CALL in current language teacher education programmes is to concentrate on the hardware and software issues rather than the pedagogy” (pp. 73–74). Thomas et al. (2013) also cautions how “technology alone cannot improve the delivery of knowledge” adding that “a new computer cannot make a teacher better. Nor can it provide a magic formula to improve learning” (p. 2). Therefore within such CALL related EFL contexts, several layers of oversight exist surrounding the employment of English-speaking teachers and their involvement in computer-mediated initiatives. These can be more readily isolated from the perspective of cause and effect.

The cause is entrenched in Japanese cultural logical and deep-seated ideological belief which awards the concept of nativity a superior status to learned competencies and developed abilities (e.g. a “native” Japanese speaker is always considered superior in linguistic knowledge and ability to a highly educated non-native Japanese speaker). The effect pertains to questionable teaching and assessment practices which facilitate the promotion of computer-mediated learning initiatives lacking pedagogical and theoretical integrity. Silencing internal calls for greater accountability is the fact that the institutional motive for maintaining such practices derives from the original cause (i.e. it is cyclical and by Japanese design). The continuation motive expresses itself in many subtle ways such as through the anticipated motivational impact and authority which speaker teachers are believed to have upon learners and language (see Houghton & Rivers, 2013).

From the outcome of the current study several recommendations can be made for practical improvement. First, the computer‐mediated English program should aim to provide research evidence for its efficacy in relation to its status as a compulsory online subject intended to maintain and improve the English language proficiency of learners (あなたの英語力の維持・向上を支援する目的でデザインされた、オンラインの必修科目です). Based on the lengthy employment duration and generous institutional support given to the English‐speaking CALL researchers, it is unacceptable that the program remains contestable and without research validation. Learners entering into higher education should be able to trust that the content, structure and assessment of compulsory initiatives is based on sound academic principles informed by more than an interest in technology. Second, the computer‐mediated English program must better legitimize itself in relation to its status as a 24‐month compulsory experience for non‐language majors. One way in which this might be achieved is to ensure that the achievement outcomes are standardized in relation to clear and coherent assessment criteria that relate to actual English language proficiencies rather than the satisfaction of instructor expectations concerning task completion (i.e. the course does not currently measure or control for what it might reasonably be expected to – English language proficiency). This necessitates that activities and tasks are formulated with clear aims and expectancies articulated to learners alongside informed assessment criteria. In addition, learners should be more systematically guided toward learning assessments and motives which reflect self‐referenced competencies with the aim of facilitating a mastery orientation and a deeper interest in the content of the program. Finally, the program would also perhaps benefit from making explicit the contemporary utility of English language proficiency development to non‐language majors. The role of English language proficiency within the day‐to‐day existence of the learners should be highlighted with explicit references given to what learners can expect to gain from developing their own English language proficiency in the short and long term.

Conclusion

The current study has tested a model of hypothesized relationships relating to motivation, strategy use and achievement within a 24-month computer-mediated English program administered at a Japanese university by three English-speaking CALL researchers. It has been shown that a performance-approach orientation has a direct effect on achievement. The mediation analysis demonstrates that learners are motivated by the externally referenced performance-approach orientation, and to a lesser extent, the self-referenced mastery orientation (albeit not in a manner that directly effects achievement outcomes). A strategic approach to learning serves as the primary variable through which the mediation effects are realized on achievement. These outcomes have then been drawn out in relation to broader concerns relating to CALL research and EFL education within the Japanese context. Practical suggestions for program improvement have also been communicated. With computer-mediated learning solutions being increased, it is hoped that the observations made within the current study are of interest to educators across multiple research domains.

Appendix. Items Retained Within the Current Study

Construct and Indicators
Performance-Approach	It’s important to me that the other students in my [program name] think that I am good at my work.
	I want to do better than other students on my [program name].
	Doing better than other students on my [program name] is important to me.
Mastery	The main reason I study [program name] is because I like to learn new things.
	I do my [program name] work because I’m interested in it.
	An important reason I do my [program name] work is because I enjoy it.
Performance-Avoidance	An important reason I do my [program name] work is so that I don’t embarrass myself.
	The reason I do my [program name] work is so my teachers don’t think I know less than other students.
	The reason I do my [program name] work is so others won’t think I’m dumb.
Deep Approach to Learning	I usually set out to understand for myself the meaning of what we have to learn on the [program name].
	When I'm reading on the [program name], I try to find out for myself exactly what the author means.
	When I’m working on a new topic on the [program name], I try to see in my own mind how all the ideas fit together.
Strategic Approach to Learning	I think I'm quite systematic and organised when it comes to revising for tests and exams on the [program name].
	I organise my [program name] study time carefully to make the best use of it.
	I work steadily through the term or semester, rather than leave my [program name] work until the last minute.
Surface Approach to Learning	I often have trouble in making sense of the things I have to remember on the [program name].
	I often worry about whether I'll ever be able to cope with the [program name] work properly.
	I often seem to panic if I get behind with my [program name] work.

Supplemental Material

sj-pdf-1-jec-10.1177_0735633121995903 - Supplemental material for A Serial Mediation Approach to Goal Orientations, Learning Strategies and Achievement Outcomes on a Computer-Mediated English Program

Supplemental material, sj-pdf-1-jec-10.1177_0735633121995903 for A Serial Mediation Approach to Goal Orientations, Learning Strategies and Achievement Outcomes on a Computer-Mediated English Program by Damian J. Rivers in Journal of Educational Computing Research

Footnotes

Acknowledgment

Gratitude is extended to the two anonymous reviewers and editor who provided critical feedback on the original version of this article. Appreciation is also expressed to Dr. Michael Vallance and Dr. Michiko Nakamura for their feedback and informed critique.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Damian J. Rivers

Note

1. The Japanese extracts shown within this article are taken verbatim from the Japanese language handbook given to learners. The English description provided is an author translation of the original Japanese wording.

Author Biography

Damian J. Rivers, PhD, is a Professor in the field of Communication at Future University Hakodate in northern Japan. He is primarily interested in quantitative educational research that aims to account for variance in academic achievement outcomes. He also researches discourse and communication in society and the application of wearable technologies within educational research.

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