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May 21, 2015 - iPhone 4) for students with low levels of English proficiency to practise vocabulary in the beginning of their Freshman English course.
Original article doi: 10.1111/jcal.12103

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Internet cognitive failure and fatigue relevant to learners’ self-regulation and learning progress in English vocabulary with a calibration scheme J.-C. Hong, M.-Y. Hwang, H.-W. Chang, K.-H. Tai, Y.-C. Kuo & Y.-H. Tsai National Taiwan Normal University, Taipei, Taiwan

Abstract

To determine the factors of learning effectiveness in English vocabulary learning when using a calibration scheme, this study developed a freshman English mobile device application (for iPhone 4) for students with low levels of English proficiency to practise vocabulary in the beginning of their Freshman English course. Data were collected and validated from 243 subjects for confirmatory factor analysis and structural equation modeling. The findings revealed that Internet cognitive failure (i.e., trait cognitive disability) was positively correlated to Internet cognitive fatigue (i.e., state cognitive disability). Both types of Internet cognitive disability were negatively correlated to self-regulation in English vocabulary learning (SREVL). SREVL was positively correlated to the degree of learning improvement. The findings implied that the use of a calibration design for mobile English vocabulary learning could benefit students with low levels of Internet cognitive disability but high levels of SREVL.

Keywords

calibration, English vocabulary learning, Internet cognitive failure, Internet cognitive fatigue, self-regulation.

Introduction

From a cognitive psychology perspective, previous researchers assert that differences in students’ cognition ability influence a multitude of educational outcomes including academic achievement (Buehl & Alexander, 2005; Greene, Torney-Purta, & Azevedo, 2010). As noted by Hertzog and Nesselroade (1987), ‘psychological variables may contain both state-like and trait-like components’ (p. 105). Trait-like components are in relation to psychological stability, such as Accepted: 7 December 2014 Correspondence: Ming-Yueh Hwang, National Taiwan Normal University, PO Box 7-513, Taipei, Taiwan. Email: [email protected] Correction added on 21 May 2015, after first online publication: The authors ‘H.-W. Chang’ and ‘Y.-H. Tsai’ were initially omitted and have now been added in the author byline.

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core personality, and are considered to be psychological traits that are consistent over time. The other type of psychological feature is a state that shows ongoing changes relevant to the time-on-task (Caspi & Roberts, 2001). From the perspective of cognitive processes, cognitive function consists of several related cognitive processes that exert control over low-level cognitive functions. As such, these cognitive processes are considered to be among the major contributing factors to learning (Friedman et al., 2008). The inhibition of cognitive functions can be divided into two types. The first type is cognitive failure, a trait of cognitive function, which occurs (Tipper, 2001) and includes directed forgetting (MacLeod, 1998). The other type is cognitive fatigue, which is defined as ‘time-related deterioration in the ability to perform certain mental tasks’ (DeLuca, 2005, p. 38). However, cognitive failure is of interest

© 2015 John Wiley & Sons Ltd

Journal of Computer Assisted Learning (2015), 31, 450–461

Internet cognitive disability and SREVL

because it measures individuals’ cognitive disability in terms of their perception, action and memory capability (Schmidt, Neubach, & Heuer, 2007). Some studies also observed a strong relationship between cognitive disabilities and suggest a linkage exists between high cognitive fatigue and cognitive failure (e.g., Larson, Alderton, Neideffer, & Underhill, 1997; Issever et al., 2008; Kass, Beede, & Vodanovich, 2010). Accordingly, the present study extended two types of cognitive disability to the Internet world, Internet cognitive failure (ICF-T) and Internet cognitive fatigue (ICF-S), to explore the effect of these two types of cognitive disability in relation to vocabulary recitation and evaluated the progress of memorization. Vocabulary learning is the basis of learning language and should substantially increase opportunities to learn and master unfamiliar syntax (see Mervis, 1983). For example, new vocabulary may be learned by morphological analysis of the individual words in facilitating individual reasoning and memory of the word’s meaning from the context. However, after learning from verbal context, ‘repetition’ or ‘rehearsal’ is necessary to learn words (Jenkins & Dixon, 1983) and enhance long-term memorization (Blachowicz & Fisher, 2000). Unfortunately, the learning approach of repetition is monotonous for most students. Hockey (2011) suggested that practising monotonous learning tasks increases cognitive fatigue more than time spent on externally imposed tasks. Much of the prior research on fatigue utilizes monotonous demanding task paradigms such as vigilance or choice reaction-time tasks (e.g., Beauducel, Brocke, & Leue, 2006). In this sense, students in the current study were asked to complete a set of tasks specific to English vocabulary memorization to explore the interrelatedness of two types of cognitive disability in relation to learning outcomes. Calibration is to judge how well learning by itemby-item judgement of learning corresponds to test performance and provides an indication of whether a person can estimate their overall recall performance accurately (Hacker, Bol, & Keener, 2008). Calibration plays a critical role in individuals’ ability to successfully self-regulate their own learning (Dinsmore & Parkinson, 2013). In addition, the quality of calibration can be associated with general cognitive ability (Alexander, 2013) and labelled as cognitive control to determine what to do next, which can influence the ongoing task (Dunlosky & Thiede, 2013). That is, suc© 2015 John Wiley & Sons Ltd

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cessful students develop regulation about how memorizing the appropriate information will influence their performance in the test (Crisp, Sweiry, Ahmed, & Pollitt, 2008). Moreover, Berger, Kofman, Livneh, and Henik (2007) argued that self-regulation particularly refers to the cognitive demands of specific situations in memory strategies. Accordingly, this research used the item-by-item calibration scheme embedded in English vocabulary learning as an externally imposed task to examine the relationship between cognitive fatigue, cognitive failure and self-regulation in English vocabulary learning (SREVL). According to Heatherton and Wagner (2011), cognitive function is related to self-regulation; they note that self-regulation can be undermined by cognitive disability to transcend negative moods and resource depletion. Self-regulation is a highly adaptive cognitive process that enables goal-directed behaviour (Banich, 2009; Zimmerman, 2000). Moreover, self-regulation in learning may be particularly strong for those who have high levels of cognitive functioning, but individuals often fail to self-regulate successfully when they suffer from chronic fatigue (Heatherton & Wagner, 2011). In e-learning research, self-regulation is invoked to account for the cognitive ability of subjects to control their learning (Fleming, Dolan, & Frith, 2012) and cognitive presence (Shea & Bidjerano, 2010). Thus, the correlation between cognitive fatigue and SREVL on the iPhone will be explored in this study. Several studies have measured cognitive control in relation to lapses and slips in daily learning. Recently, advanced simulation equipment has incorporated learning technology through smartphone applications. However, few studies have examined cognitive disability in specific tasks, such as Internet-related English learning. Consistent with this gap, the purpose of this study is first, to develop a conceptual framework for identifying the role of individual differences relevant to Internet cognitive failure and fatigue to connect twofold self-regulation and the degree of learning progress by applying a ‘calibration’ scheme to English vocabulary learning in iPhone applications. Second, to determine the validity of the pathway by testing the correlation between the users’ two types of cognitive disabilities (ICF-T and ICF-S) when predicting the self-regulation relevant to the learning progress of college students. However, this article will first examine the relevant literature in relation to Internet

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Figure 1 Screenshot of English Vocabulary Learning System

cognitive failure and fatigue, calibration, and selfregulation and lay out the hypotheses. The research design, including the measure questionnaire, will then be explained, followed by a discussion of the results. Design for English vocabulary learning on iPhone

Alexander (2013) referred to calibration as the individual gauging ‘whether subsequent efforts will substantially affect progress and performance’. Winne (2004) proposed two meanings for calibration: (1) the checking of a measuring instrument against an accurate standard to determine any deviation and correct for errors; and (2) a mark showing one of the units of measurement on a measuring instrument (p. 466). He highlights the second meaning of calibration as a metric ‘measure’ of a feature of some object or event. That is, calibration refers to the accuracy of or alignment between a judgement and a meaningful standard (Winne & Muis, 2011). In many cases, the meaningful standard is the test item’s performance (Boekaerts & Rozendaal, 2010; Koriat, 2012). According to the second calibration feature, this study focused on calibration with respect to the learners’ self-evaluation of their recognition of English vocabulary. The proposed English vocabulary learning system was designed to assess long-term memory via an efficient calibration scheme using a preview or review process for individual learners; it was developed through EVL@S (i.e., English vocabulary learning at Shi-Ta-Taiwan Normal University).

An item-by-item monitoring task was assessed in this study; that is, retrospective judgements involving the self-assessment of a memory were produced (e.g., Kelley & Sahakyan, 2003). In this study, the learner must answer the question ‘Do I remember the Chinese meaning of this English vocabulary word?’ in a yes or no format. In Figure 1, the number on the top left corner indicates the number of times the participants replied ‘remember’ (89 times in Figure 1), and the number to the right indicates the total number of correctly answered vocabulary words (87 in Figure 1). A mobile learning application is a helpful supplement to an existing learning context, such as a course. The system includes two courses containing 2054 vocabulary words: ‘1000 basic vocabulary words’, adapted from the Ministry of Education, and 1054 words at the level of a ‘C English course for university freshman’, which was designed for low-level English proficiency learners in this study. Literature review

Cognition is an important aspect of learning research. Cognitive load theory proposes that working memory can only hold a limited amount of information or perform a limited number of tasks simultaneously (Paas, Renkel, & Sweller, 2004). When undertaking tasks, the quantity of information and interactions that must be processed can lead to either cognitive failure (Ginns, 2006) or fatigue (Merckelbach, Muris, Rassin, & Horselenberg, 2000) of the finite level of working © 2015 John Wiley & Sons Ltd

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memory. More importantly, this examination of calibration intentionally focused on two types of cognitive disability in relation to self-regulation, and was examined in relation to learning progress in exploring the effect of learning using a calibration process on the iPhone. Calibration and learning progress

In the feeling of knowing paradigm (Hart, 1965), participants were asked to estimate the likelihood of subsequently recognizing a piece of information that they had failed to recall earlier. The implicit logic is that the better one understands his or her memory, the more accurate the predictions are likely to be (Sacher, Taconnat, Souchay, & Isingrini, 2009). Well-calibrated students often perform better, make more accurate calibration judgements, and are able to use information gained during testing to estimate their performance level (Nietfeld & Schraw, 2002). That is, students who are able to predict test performance will perform more accurately on the test (Hadwin & Webster, 2013). Hence, this study focuses on calibration with respect to learners’ self-evaluations of their learning progress as the basis for exploring the interrelatedness among these research variables. Self-regulation and learning progress

In the current research, self-regulation is viewed as a process that can help students to improve learning achievement (Zimmerman & Schunk, 2001). In addition, the earlier studies in educational psychology accumulate consistent empirical evidence supporting the statement that self-regulation, cognitive processing strategies and goal orientation are important predictors of academic performance (e.g., Minnaert & Janssen, 1999; Simons, Dewitte, & Lens, 2004; Vermunt, 2005). However, learning processes that occur during learning efforts that affect concentration and performance are considered to occur during the cognitive process in learning. Accordingly, all of this research has paved the way for us to understand academic self-regulation and has important implications for learning practice, as in the vocabulary learning in this study. Thus, the first hypothesis is proposed as follows. H1: Self-regulation is significantly correlated to the degree of learning progress. © 2015 John Wiley & Sons Ltd

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Internet cognitive fatigue’s relevance to self-regulation

Cognitive fatigue refers to a decline in mental efficiency and the accompanying feelings of strain and weariness that occur during time-on-task (Christodoulou, 2005; DeLuca, 2005). It includes neglectfulness, loss of memorized information, distracted attention and loss of concentration. Several studies provide evidence to support the notion that subjective fatigue serves as a correlation or expression of a decline in mental efficiency and performance (e.g., Lorist et al., 2000). Muscio (1921) noted that subjective fatigue may confound study of the expression of fatigue-related performance decrements. However, studies using the self-regulation paradigm typically only examine changes in cognitive fatigue that result from the mental performance of over time-on-task (e.g., Ackerman, 2011). Cognitive fatigue is not merely an outcome associated with mental work, but more generally represents a crucial element in the effortregulation system as a whole (Kanfer, 2011). Thus, this study focused on examining the association of Internet cognitive fatigue with SREVL. The following hypothesis is proposed: H2: Internet cognitive fatigue is significantly correlated to SREVL. Internet cognitive failure relevant to self-regulation

Cognitive failure refers to a departure from functional cognitive operation. It includes neglectfulness, loss of memorized information, distractibility and a lack of ideas (Broadbent, Cooper, Fitzgerald, & Park, 1982). Such failures may be the result of distracting external stimuli (e.g., loud noises), internal thoughts and distractions (e.g., daydreaming), and mind wandering or absent-mindedness that may lead to mistakes making in completing a task (e.g., Wallace, Kass, & Stanny, 2002). Zimmerman (1994) synthesized self-regulation as the result of attention focusing, which emphasizes the need for learners to protect their attention from distractions. On the other hand, self-instruction involves telling oneself how to proceed during a task and selfmonitoring involves keeping track of the task progress. According to the online cognitive-behavioural model, symptoms of cognitive failure are the determinants of underlying online cognitive processes

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(Caplan, 2010). As the designing of vocabulary learning in iPhone shows, a belief of self-regulated learning may be associated with the effects of ICF-T from using the mobile device for learning. Therefore, this study focused on examining the correlation between ICF-T and SREVL. The following hypothesis is proposed: H3: Internet cognitive failure is significantly correlated to SREVL. Internet cognitive failure relevant to Internet cognitive fatigue

The cognitive inhibition model implies that prime distractors are first briefly activated and cause cognitive failure; then after a period of time, cognitive fatigue follows (Laplante, Everett, & Thomas, 1992; Lowe, 1985). Moreover, Ackerman and Kanfer (2009) extended Kahneman’s (1973) ‘circuit’ model of cognitive inhibition to the study of cognitive failure to suggest that cognition is associated with sustained task performance, which may also be relevant to individual cognitive disability, and this is the determinant of the underlying online cognitive processes (Caplan, 2010; Kanfer, 2011). This belief may also be associated with diminished effects from using a mobile device for learning. Thus, the hypothesis is proposed as follows. H4: Internet cognitive failure is significantly correlated to Internet cognitive fatigue. Research design

According to the above research hypotheses, four constructs (i.e., Internet cognitive fatigue, Internet cognitive failure, self-regulation in English learning and degree of learning progress) were designed to guide this study. These constructs were applied to data from college students with a low level of English proficiency according to their entry-examination ranking, and then attributed in a mobile learning with embedded calibration scheme. Research procedure

The participants were enrolled in different departments in the fall of 2013 but were with the same English instructor. To ensure the familiarity of the structural and technological aspects of EVL@S, an e-mail was

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delivered to all freshmen to introduce the operational details and to invite them to join this research. If they did not have an iPhone 4, one was provided by the research team for the purpose of the study. Therefore, students from five different classes were asked to practise 1054 words at the level of a ‘C English course for university freshman’ through EVL@S for at least 10 min/day for 6 weeks in the beginning of their Freshman English course under the English teacher’s approval. In terms of ethics, the students did not have to provide personal information except for gender, and they were aware that they were taking part in an evaluation study and that the data they provided were anonymous. In addition, to encourage participation, the participants were informed that if they completed the assignment and the questionnaire on Google Docs, they would receive a convenience store coupon as a compliment. Research participants

The participants were freshmen studying at a technology college near Taipei city and aged approximately 19–20; their English proficiency was in the lower level according to the ranking of their college entry examination. Two weeks after the completion of the English vocabulary learning tasks from EVL@S, a total of 323 participants returned 264 questionnaires; data from only 243 questionnaires were validated for the data analysis. From the 243 participants, men accounted for 69.5% and women accounted for 30.5%. Data analysis of learning progress

A pre-test and post-test were given to explore the degree of learning progress. To determine the pre-game English vocabulary ability, the participants were given a pre-test where 50 test items were randomly suggested from the 1054 words at the level of a ‘C English course for university freshman’ before the implementation of the experiment. After six occasions of practice with EVL@S, students were then asked to complete a posttest with another 50 test items. The average pre-test score was M = 25.64, and the standard deviation was SD = 9.22. The average post-test score was M = 40.50, and the standard deviation was SD = 10.41. The average progress score was indexed by subtracting the pre-test from the post-test. The progress © 2015 John Wiley & Sons Ltd

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average was M = 14.86, and the standard deviation was SD = 5.09, showing that the average post-test grade was 16.86 higher than the pre-test grade. Research instruments

An EVL@S questionnaire was adopted using a 5-point Likert scale. In this study, items were partially adapted and subjected to confirmatory factor analysis. Measuring questionnaire

The Internet cognitive fatigue measurement This measure was adapted from Ackerman and Kanfer’s (2009) scale and Chalder et al.’s (1993) scale. It was designed to measure the cognitive fatigue from time-on-task in attention, concentration, memory, perception and motor control and to assess the taskspecific mistakes as ‘time-related deterioration in the ability’ (DeLuca, 2005). Accordingly, statements for five items were created in this study. Internet cognitive failure measurement This measure was adapted from the scale developed by Hong et al. (2014). It was designed to measure the failures in memory, perception and motor control and to assess the frequency of particular mistakes in using Internet to learn or work, such as attention lapse and becoming unintentionally distracted. Self-regulation in learning English vocabulary measurement The questionnaire items for self-regulation in learning English vocabulary were adapted from Zimmerman’s (1994) definition of self-regulation. The components that function in relation to this dimension are control, goal structures and self-directedness and were designed to measure attention focusing, self-instruction and self-monitoring.

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0.938, surpassing the suggested threshold value of 0.7 (Hair, Black, Babin, & Anderson, 2009). Second, convergent validity refers to the degree to which multiple items measure one construct. Convergent validity in the present study was evaluated by verifying if: (1) the average variance extracted (AVE) values were above 0.5 (Fornell & Larcker, 1981); and (2) the factor loadings of all items were significant and above 0.5 (Hair et al., 2009). All of these conditions were met in the study, indicating acceptable convergent validity. Third, the discriminative power of the scale was determined by its ability to discriminate the items of instrument, and was examined by independent t-test to explain the discriminative power of each item. If the critical ratio (t-value) is larger than 3, the discriminative power is significant, Table 1 shows that all t-values were larger than 3 (p < .001***), indicating the subscales also all reached significance, thus suggesting that all items were discriminative (Green & Salkind, 2004). Fourth, the construct validity of the research instruments was established using a confirmatory factor analysis (Byrne, 2001). All factor loadings were statistically significant and ranged from 0.701 to 0.908. Fifth, to evaluate the consistency of the variables, the reliability of the questionnaire was assessed using Cronbach’s alpha. According to Hancock and Mueller (2006), a Cronbach’s alpha value above 0.6 indicates an acceptable level of reliability. Table 1 also shows the Cronbach’s alpha values and indicates that all values were above 0.6. The results show that the alpha values for ICF-T, ICF-S and SREVL were 0.924, 0.921 and 0.893, respectively. The reliability coefficient for the entire questionnaire was 0.909, which suggests that the variables were reliable. Finally, Table 1 presents the composite validity of the entire questionnaire, which was 0.909, indicating that the variables were reliable. Table 1 also shows that all of the mean values for each dimension were between 2.54 and 3.53 and that the standard deviations were small, indicating a low degree of dispersion.

Reliability and validity analysis

A confirmatory factor analysis was first applied to examine the factor loading, reliability and validity of the research instruments. First, internal consistency can be determined by examining the composite reliability (CR) of the constructs (Fornell & Larcker, 1981), and all CR values in the present study ranged from 0.923 to © 2015 John Wiley & Sons Ltd

Path analysis

Previous studies on calibration indicate that convincingly learners potentially are assumed that they can use calibration as a frame to acquire knowledge (Pieschl, 2009). In addition, in using calibration, self-regulation is applied to moderate learning effectiveness (Winne,

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Table 1. Reliability and Validity Analysis Items

M

Internet cognitive failure (ICF-T): M = 2.54, SD = .67, CR = .938, AVE = .752, α = .924 1. I often misinterpret the messages’ meaning so that I must read it again. 2. I often have difficulty finding the information I need on the web page. 3. If there are too many messages on the screen, I always miss information although it is there. 4. I often miss the location of my posts on the Internet. 5. I often forget the message I posted. Internet cognitive fatigue (ICF-S): M = 3.06, SD = 1.04, CR = .932, AVE = .734, α = .921 1. I lose concentration very quickly when I learn on EVL@S. 2. I reach attention deficit very quickly when I learn on EVL@S. 3. The speed at which I process information slows down quickly when I learn on EVL@S. 4. I have difficulty recalling what I have done after a short time when I learn on EVL@S. 5. I have difficulty making mental shifts (e.g., cannot work multiple jobs or messages at a time) after a short time when I learn on EVL@S. Self-regulation in English vocabulary learning (SREVL): M = 3.53, SD = .76, CR = .923, AVE = .708, α = .893 1. I apply specific strategies to enhance my vocabulary memorization. 2. I modify my goals by reducing the number of vocabulary words to recite when I encounter difficulty memorizing vocabulary. 3. I pay attention to the difficult-to-memorize vocabulary and copy it to a worksheet to increase my memorization. 4. Based on previous practice and tests, I adjust my level of effort for the unfamiliar vocabulary. 5. I confirm my understanding of a vocabulary word before reciting it if I make an error on a test.

2004). Winne further stated that the retention of information over time is affected by the cognitive function applied by learners in relation to any particular feature of the instructional design. That is, learning effectiveness in relation to calibration design features in a mobile learning environment determines the forms of cognition applied to learning. Therefore, the pathway analysis of this study in relation to predictability as the cognitive variables mediated the antecedent variables, and reflected on learning progress by using calibration was tested through partial least squares (PLS). This study used structural equation modeling (SEM) implemented in PLS for data analysis, because PLS is a desirable research tool as it requires a small number of samples and places less restrictive demands on residual distribution (Cheung, Luo, Sia, & Chen, 2009). Figure 2 shows the results of the path relationship among the hypotheses. It is evident that all hypotheses were supported. Table 2 indicates that the ICF-T influenced the participant’s ICF-S with a standardized regression coefficient (SRC) of 0.42 (t = 6.50). The

SD

Loading

t-value

2.26 2.26 2.28

.96 .93 .88

.887 .908 .881

36.72 37.93 40.43

2.18 3.72

.92 .93

.892 .760

37.21 62.11

2.94 3.06 3.25

1.25 1.21 1.23

.830 .894 .900

36.74 39.45 41.34

3.00

1.17

.892

40.16

3.07

1.16

.842

41.18

3.75 3.53

.95 .93

.701 .874

61.62 59.22

3.52

.87

.901

63.13

3.28

.92

.874

55.28

3.55

.88

.841

62.70

ICF-S and ICF-T influenced and supported SREVL with an SRC of −0.14 (t = −2.91) and −0.69 (t = −18.20). Last, the SREVL influenced and supported Degree of learning progress (DLP) with an SRC of 0.82 (t = 28.51). In addition, the explanatory power of ICF-T versus ICF-S was 18%. The explanatory

Figure 2 Verification of the Research Model

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Table 2. Test of Hypotheses Hypothesis

Path

SRC

t-value

Support

H1 H2 H3 H4

SREVL→ DLP ICF-S→ SREVL ICF-T→ SREVL ICF-T→ ICF-S

.82 −.14 −.69 .42

28.51 −2.91 −18.20 6.50

Positive Negative Negative Positive

power of ICF-S and ICF-T versus SREVL was 58%. The explanatory power of SREVL versus DLP was 68%. Hence, the dependent variable in the research provided reasonable predictive ability (Hair et al., 2009) (Table 2). The results of the hypothesis testing are presented in Table 2. According to Byrne (2001), all hypotheses in this study were supported. Moreover, ICF-T served as a positive predictor of ICF-S, and SREVL served as a positive predictor of DLP, whereas from ICF-T to SREVL and from ICF-S to SREVL they were both negative predictors.

Discussion

The empirical findings of this study showed that the two types of cognitive disability, ICF-T and ICF-S, contributed significantly to predicting SREVL and DLP. Overall, the findings extended our understanding of the nature of two types of Internet cognitive disability experienced by technology college students when using the EVL@S device to learn C-level English vocabulary. The results of this study revealed that the all-over mean of the ICF-T construct was below 2.54. The mean of ICF-S was 3.06, a little higher than ICF-T, indicating that the ICF-T of those students was not inhibited at the beginning but that as the practice time increased, the ICF-S also increased when using the calibration scheme to learn English vocabulary. Several studies provide evidence to support the notion that subjective fatigue primarily serves as a correlate or the expression of a decline in mental efficiency (e.g., Lorist et al., 2000). Supported by the feeling of knowing paradigm (Hart, 1965), the participants were asked to estimate the likelihood of subsequently recognizing a piece of information. Accordingly, the device used in this study calculated and produced figures to illustrate the difference between the estimate of knowing and the failure to recall as a reference for calibrating the next attempt. © 2015 John Wiley & Sons Ltd

Table 2 shows that both dimensions of ICF-T and ICF-S were negatively correlated with SREVL. The results of the study confirmed the hypothesis, as ICF-S accounted for a greater percentage of the negative decrement variance in SREVL. This result is consistent with Di Fabio and Palazzeschi’s (2013) study, which reveals that cognitive fatigue can negatively predict ones’ cognitive decisiveness. The results of that study also showed that perceived ICF-T explained a greater percentage of the decrement variance in the SREVL. Some studies highlight that cognitive functions fundamentally refer to SREVL and encompass working memory, inhibitory control and cognitive flexibility (e.g., Bassett, Denham, Wyatt, & Warren-Khot, 2012; Denham, Warren-Khot, Bassett, Wyatt, & Perna, 2012). These findings assert that SREVL requires the use of cognitive function (Denham et al., 2012) and support the result of this study. Alexander (2013) explained that calibration is a natural component of learning programs in which students set goals for performance in a learning course. Taking this self-regulated perspective, this study examined whether learners’ SREVL was relevant to the calibration based on DLP when learning vocabulary. The results of this study revealed that a higher level of SREVL resulted in a higher DLP. This result is consistent with the assertion of Hadwin and Webster (2013) that calibration can be used as the basis for learning improvement. Conclusions

Cognitive performance involves a number of component processes, such as stimulus detection, information encoding, working memory and motor action. Each of the underlying cognitive processes and how those cognitive processes interact affect the overall performance. After entangling the cognitive load effects of calibration learning, the research model used cognitive failure and fatigue to predict the calibration performance mediated by self-regulated learning of English

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vocabularies in mobile learning platform. The present study discovered that subjective self-judgement through calibration and individual SREVL are the key factors affected by cognitive disabilities in English vocabulary learning. Briefly, the results indicated that increasing Internet cognitive failure will increase Internet cognitive fatigue; both types of cognitive disability were negatively correlated to SREVL in English vocabulary learning. Individuals’ SREVL can also predict their learning progress in terms of the increase in vocabulary memorization after practising. Contributions

Taken together, the findings of this study suggested three principal contributions. First, this study introduced Internet cognitive failure and fatigue, two important constructs relevant to the study of cognitive processes in learning. In particular, this study highlighted the correlation between trait and state cognitive disability, ICF-T can positively predict ICF-S. This area is rarely studied in the current mobile learning environment; thus, this study can be identified as a foundation for future studies. Second, this study took further support to perspectives on calibration. Prior research establishes the importance of relational characteristics when using calibration to improve different learning effectiveness mediated by self-regulation; this study advanced the understanding of relational dynamics by showing how SREVL with respect to using calibration scheme can promote the relationship between cognitive ability and the degree of improvement in vocabulary learning. Third, prior to this study, technology college students rarely received objective external evaluations of their progress during the online study of English vocabulary. This study supported an expanded perspective on the use of calibration in learning English vocabulary given individual SREVL. Given the importance of these individual empirical contributions to understanding the nature of using calibration to design online language learning courses for iPhone 4, this study posits educational researchers to further elucidate calibration. Thus, it may be important to use EVL@S to provide performance self-evaluation in other language vocabulary learning by students with various levels of English competency.

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Future studies

Although the results of the study appeared promising due to the underlying role of perceived cognitive factors in SREVL, several limitations must be noted. For example, one limitation was the impossibility of generalizing the results beyond the specific sample of the technology college students in Taiwan who participated in the study. Future research should use samples that represent other populations, and the results of international studies must be examined and compared with those reported here. Self-regulation refers to any effort on the part of an organism to alter thoughts, feelings or behaviours (Baumeister, Heatherton, & Tice, 1994). For individuals to successfully self-regulate, they must not only have appropriate goals and the means to monitor progress towards them, but they must also be able to operate upon themselves or their learning environment to bring about the desired changes. These criteria are based on the idea that accurate monitoring allows for optimal self-regulation in memory behaviour (Koriat, 2000). In this regard, the focus for future design could be to record the desired outcome and change the self-regulation, thereby exploring the effectiveness of changing self-regulation on learning improvement. Computer technologies for education have significantly progressed, including those for language learning (Ditcharoen, Naruedomkul, & Cercone, 2010), and the use of new technology devices for language learning has prevailed in different learning contexts. To promote the effectiveness of vocabulary learning, various calibration schemes using different technology devices should be implemented to explore the effect of memorization and in other language domains, such as learning English grammar. Acknowledgements

This research was partially supported by the ‘Aim for the Top University Project’ of National Taiwan Normal University (NTNU), sponsored by the Ministry of Education, Taiwan, and the ‘International ResearchIntensive Center of Excellence Program’ of NTNU and Ministry of Science and Technology, Taiwan (MOST 103-2911-I-003-301 and MOST 101-2511-S-003-056MY3). © 2015 John Wiley & Sons Ltd

Internet cognitive disability and SREVL

References Ackerman, P. L. (2011). 100 years without resting. In P. L. Ackerman (Ed.), Cognitive fatigue: The current status and future for research and applications (p. 11–43). Washington, DC: American Psychological Association. Ackerman, P. L., & Kanfer, R. (2009). Test length and cognitive fatigue: An empirical examination of effects on performance and test-taker reactions. Journal of Experimental Psychology: Applied, 15, 163–181. Alexander, P. A. (2013). Calibration: What is it and why it matters? An introduction to the special issue on calibrating calibration. Learning and Instruction, 24, 1–3. Banich, M. T. (2009). Executive function: The search for an integrated account. Current Direction in Psychology Science, 18, 89–94. Bassett, H. H., Denham, S., Wyatt, T. M., & Warren-Khot, H. K. (2012). Refining the preschool self-regulation assessment for use in preschool classrooms. Infant and Child Development, 21, 596–616. Baumeister, R. F., Heatherton, T. F., & Tice, D. M. (1994). Losing control: How and why people fail at selfregulation. San Diego, CA: Academic Press. Beauducel, A., Brocke, B., & Leue, A. (2006). Energetical bases of extraversion: Effort, arousal, EEG, and performance. International Journal of Psychophysiology, 62(2), 212–223. Berger, A., Kofman, O., Livneh, U., & Henik, A. (2007). Multidisciplinary perspectives on attention and the development of self-regulation. Progress in Neurobiology, 82(5), 256–286. Blachowicz, C., & Fisher, P. (2000). Vocabulary instruction. In M. Kamil, P. Mosenthal, P. D. Pearson, & R. Barr (Eds.), Handbook of reading research (pp. 503–523). Mahwah, NJ: Lawrence Erlbaum Associates. Boekaerts, M., & Rozendaal, J. S. (2010). Using multiple calibration indices in order to capture the complex picture of what affects learns’ accuracy of feeling of confidence. Learning and Instruction, 20, 372–382. Broadbent, D. E., Cooper, P. F., Fitzgerald, P., & Park, K. R. (1982). The cognitive failures questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21, 1–16. Buehl, M. M., & Alexander, P. A. (2005). Motivation and performance differences in students’ domain-specific epistemological belief profiles. American Education Research Journal, 42, 697–726. Byrne, B. M. (2001). Structural equation modeling with Amos: Basic concepts, applications and programming. Mahwah, NJ: Lawrence Erlbaum Associates.

© 2015 John Wiley & Sons Ltd

459

Caplan, S. E. (2010). Theory and measurement of generalized problematic Internet use: A two-step approach. Computers in Human Behavior, 26, 1089–1097. Caspi, A., & Roberts, B. W. (2001). Personality development across the life course: The argument for change and continuity. Psychological Inquiry, 12, 49–66. Chalder, T., Berelowitz, G., Pawlikowska, T., Watts, L., Wessely, S., Wright, D., & Wallace, E. P. (1993). Development of a fatigue scale. Journal of Psychosomatic Medicine, 37(2), 147–153. Cheung, M. Y., Luo, C., Sia, C. L., & Chen, H. (2009). Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. Journal of Electronic Commerce, 13(4), 9–38. Christodoulou, C. (2005). The assessment and measurement of fatigue. In J. DeLuca (Ed.), Fatigue as a window to the human brain (pp. 19–36). Cambridge, MA: MIT Press. Crisp, V., Sweiry, E., Ahmed, A., & Pollitt, A. (2008). Tales of the expected: The influence of students’ expectations on question validity and implications for writing exam questions. Educational Research, 50, 95–115. DeLuca, J. (2005). Fatigue, cognition, and mental effort. In J. Deluca (Ed.), Fatigue as a window to the human brain (pp. 37–57). Cambridge, MA: MIT Press. Denham, S. A., Warren-Khot, H. K., Bassett, H. H., Wyatt, T., & Perna, A. (2012). Factor structure of self-regulation in preschoolers: Testing models of a field-based assessment for predicting early school readiness. Journal of Experimental Child Psychology, 111, 386–404. Di Fabio, A., & Palazzeschi, L. (2013). Incremental variance in indecisiveness due to cognitive failure compared to fluid intelligence and personality traits. Personality and Individual Differences, 54(2), 261–265. Dinsmore, D. L., & Parkinson, M. M. (2013). What are confidence judgments made of? Students’ explanations for their confidence ratings and what that means for calibration. Learning and Instruction, 24, 4–14. Ditcharoen, N., Naruedomkul, K., & Cercone, N. (2010). SignMT: An alternative language learning tool. Computers & Education, 55(1), 118–130. Dunlosky, J., & Thiede, K. W. (2013). Four cornerstones of calibration research: Why understanding students’ judgments can improve their achievement. Learning and Instruction, 24, 58–61. doi:10.1016/j.learninstruc.2012 .05.002 Fleming, S. M., Dolan, R. J., & Frith, C. D. (2012). Metacognition: Computation, biology and function. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367(1594), 1280–1286.

460

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology. General, 137(2), 201–225. Ginns, P. (2006). Integrating information: A meta-analysis of the spatial contiguity and temporal contiguity effects. Learning and Instruction, 16, 511–525. Green, S. B., & Salkind, N. (2004). Using SPSS for Windows and Macintosh: Analyzing and understanding data (4th ed.). Englewood Cliffs, NJ: Prentice-Hall. Greene, J. A., Torney-Purta, J., & Azevedo, R. (2010). Empirical evidence regarding relations among a model of epistemic and ontological cognition, academic performance, and educational level. Journal of Educational Psychology, 102(1), 234–255. Hacker, D. J., Bol, L., & Keener, M. C. (2008). Metacognition in education: A focus on calibration. In J. Dunlosky & R. A. Bjork (Eds.), Handbook of metamemory and memory (pp. 429–455). New York, NY: Taylor & Francis. Hadwin, A. F., & Webster, E. A. (2013). Calibration in goal setting: Examining the nature of judgments of confidence. Learning and Instruction, 24, 37–47. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. Hancock, G. R., & Mueller, R. O. (2006). Structural equation modeling: A second course. Greenwich, CT: Information Age Publishing. Hart, J. T. (1965). Memory and the feeling-of-knowing experience. Journal of Educational Psychology, 56, 208– 216. Heatherton, T. F., & Wagner, D. D. (2011). Cognitive neuroscience of self-regulation failure. Trends in Cognitive Sciences, 15(3), 132–139. Hertzog, C., & Nesselroade, J. R. (1987). Beyond autoregressive models: Some implications of the trait-state distinction for the structural modeling of developmental change. Child Development, 58, 93–109. Hockey, G. R. J. (2011). A motivational control theory of fatigue. In P. L. Ackerman (Ed.), Cognitive fatigue: The current status and future for research and applications. Washington, DC: American Psychological Association. Hong, J. C., Hwang, M. Y., Tai, K. S., & Chen, Y. L. (2014). The design of calibration to enhance self-confidence in English vocabulary learning by means of mobile devices. Computer & Education, 72, 313–322.

J.-C. Hong et al.

Issever, H., Özdilli, K., Önen, L., Tan, O., Disci, R., & Yardimici, O. (2008). Examination of personal factors in work accidents. Indoor and Built Environment, 1, 562– 566. Jenkins, J. R., & Dixon, R. (1983). Vocabulary learning. Contemporary Educational Psychology, 8(3), 237– 260. Kahneman, D. (1973). Attention and effort. Englewood Cliffs, NJ: Prentice-Hall. Kanfer, R. (2011). Determinants and consequences of subjective cognitive fatigue. In P. L. Ackerman (Ed.), Cognitive Fatigue: The current status and future for research and applications. Washington, DC: American Psychological Association. Kass, S. J., Beede, K. E., & Vodanovich, S. (2010). Selfreport measures of distractibility as correlates of simulated driving performance. Accident Analysis and Prevention, 42(3), 874–880. Kelley, C. M., & Sahakyan, L. (2003). Memory, monitoring, and control in attainment of memory accuracy. Journal of Memory and Language, 48, 704–721. Koriat, A. (2000). The Feeling of Knowing: Some metatheoretical implications for consciousness and control. Consciousness and Cognition, 9, 149–171. Koriat, A. (2012). The self-consistency model of subjective confidence. Psychological Review, 119, 80–113. Laplante, L., Everett, J., & Thomas, J. (1992). Inhibition through negative priming with Stroop stimuli in schizophrenics. British Journal of Clinical Psychology, 31, 307– 326. Larson, G., Alderton, D., Neideffer, M., & Underhill, E. (1997). Further evidence on the dimensionality and correlates of the Cognitive Failures Questionnaire. British Journal of Psychology, 88, 29–38. Lorist, M. M., Klein, M., Nieuwenhuis, S., De Jong, R., Mulder, G., & Meijman, T. F. (2000). Mental fatigue and task control: Planning and preparation. Psychophysiology, 37, 614–625. Lowe, D. G. (1985). Further investigations of inhibitory mechanisms in attention. Memory and Cognition, 13, 74–80. MacLeod, C. M. (1998). Directed forgetting. In J. M. Golding & C. M. MacLeod (Eds.), Intentional forgetting: Interdisciplinary approaches (pp. 1–57). Mahwah, NJ: Lawrence Erlbaum Associates. Merckelbach, H., Muris, P., Rassin, E., & Horselenberg, R. (2000). Dissociative experiences and interrogative suggestibility in college students. Personality and Individual Differences, 29, 1133–1140. Mervis, C. B. (1983). Acquisition of a lexicon. Contemporary Educational Psychology, 8, 210–236.

© 2015 John Wiley & Sons Ltd

Internet cognitive disability and SREVL

Minnaert, A., & Janssen, P. J. (1999). The additive effect of regulatory activities on top of intelligence in relation to academic performance in higher education. Learning and Instruction, 9(1), 77–91. Muscio, B. (1921). Is a fatigue test possible? British Journal of Psychology, 12, 31–46. Nietfeld, J. L., & Schraw, G. (2002). The effect of knowledge and strategy explanation on monitoring accuracy. Journal of Educational Research, 95, 131–142. Paas, F., Renkel, A., & Sweller, J. (2004). Cognitive Load Theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32, 1–8. Pieschl, S. (2009). Metacognitive calibration – An extended conceptualization and potential applications. Metacognition and Learning, 4, 3–31. Sacher, M., Taconnat, L., Souchay, C., & Isingrini, M. (2009). Divided attention at encoding: Effect on feeling-ofknowing. Consciousness and Cognition, 18(3), 754–761. Schmidt, K. H., Neubach, B., & Heuer, H. (2007). Selfcontrol demands, cognitive control deficits and burnout. Work and Stress, 21, 142–154. Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55(4), 1721–1731. Simons, J., Dewitte, S., & Lens, W. (2004). The role of different types of instrumentality in motivation study strategies, and performance: Know why you learn, so you’ll know what you learn! British Journal of Educational Psychology, 74(3), 343–360.

© 2015 John Wiley & Sons Ltd

461

Tipper, S. P. (2001). Does negative priming reflect inhibitory mechanisms? A review and integration of conflicting views. Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, 54(2), 321– 343. Vermunt, J. (2005). Relations between student learning patterns and personal and contextual factors and academic performance. Higher Education, 49(3), 205–234. Wallace, J. C., Kass, S. J., & Stanny, C. J. (2002). The cognitive failures questionnaire revisited: Dimensions and correlates. Journal General Psychology, 129, 238– 256. Winne, P. H. (2004). Students’ calibration of knowledge and learning processes: Implications for designing powerful software learning environments. International Journal of Educational Research, 41, 466–488. Winne, P. H., & Muis, K. R. (2011). Statistical estimates of learners’ judgments about knowledge in calibration of achievement. Metacognition and Learning, 6, 179–193. Zimmerman, B. J. (1994). Dimensions of academic selfregulation: A conceptual framework for education. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulation of learning and performance (pp. 3–24). Hillsdale, NJ: Lawrence Erlbaum Associates. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego, CA: Academic Press. Zimmerman, B. J., & Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.