Elsevier

Computers in Human Behavior

Volume 63, October 2016, Pages 604-612
Computers in Human Behavior

Full length article
Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations

https://doi.org/10.1016/j.chb.2016.05.084Get rights and content

Highlights

  • A path model tested the relationship of technology use and academic performance.

  • Examined interrelations of technology with engagement and self-directed learning.

  • Results indicated strong support for the model matching hypotheses.

  • Technology use has positive effect on self-directed learning & student engagement.

  • Academic performance affected indirectly by technology via self-directed learning.

Abstract

The widespread technology use among current college and university students has made higher educational institutions worldwide acknowledge the need of incorporating it in teaching and learning for explicit reasons. But does access and usage of technology enhance academic performance and foster student engagement in reality? Researches in the last over two decades have conjectured both the positive and negative outcomes of the students’ continuous interface with technology. Student engagement and self-directed learning (SDL) are the two other themes that have independently attracted considerable interest of researchers, ascribable to the explicit and implicit assertions that both are related to the academic success. Additionally, the relationship of technology use with these two academic behaviors have also been investigated although not very extensively. The current study aimed to inspect a path model with technology use, student engagement, self-directed learning and academic performance among undergraduate students. 761 students responded to an online survey comprising three scales: Media and Technology Usage and Attitude Scale (MTUAS), Self-Rating Scale of Self-Directed Learning (SRSSDL), and student version of Utrecht’s Work Engagement Scale (UWES-S). The results showed that use of technology has a direct positive relationship with students’ engagement and self-directed learning, however, no significant direct effect was found between technology use and academic performance. The findings point towards the complex interchange of relationships of the students’ technology use with student engagement, self-directed learning and academic performance. The implications and future research directions are discussed.

Introduction

The present day college and university students comprise of a generation who are brought up in a digitally rich environment and engrossed in a world permeated with various types of Information and Communication Technologies (ICTs). However, the actual usage of these technologies for the academic purposes, by this technologically-revolutionized era-generation variously termed as “Millennials” or Gen Y (Howe & Strauss, 1991), “the net generation” (N-Gen; Tapscott, 1998), “digital learners” (Brown, 2000), digital natives (Prensky, 2001), “learners of the digital era” (Rapetti & Cantoni, 2010) etc., are continually debated. Does the persistent absorption and engagement with technology facilitate or hamper their learning experiences and academic performance? Given the fact that technology affordances are in concurrence with learning environments, i.e., from retrieving and sharing information to instant access and interaction with faculty and peers, it seems plausible that students may be using various technologies to facilitate and augment their learning experiences and effectively meeting academic challenges. However, the opinions vary: on the one end of the spectrum are those holding the belief that digital technology does augment and actually have already ‘transformed’ the teaching and learning in higher education (e.g., Beetham & Sharpe, 2013); on the other end are those who perceive these technologies as ‘disruptive’, and thereby a challenge for the universities to cope with, as Losh (2014) observes, “Not all modes of digital engagement are suited to education”. Nonetheless, it is apparent that the ubiquitous and ever evolving digital technology has infiltrated in the ecosystem of the higher education, and explicably got the attention of researchers’ focusing on numerous related issues, particularly the effectiveness and efficacy of technology vis-à-vis learning and teaching processes and outcomes.

The effect of technology with regard to students’ academic achievement is persistently marked in growing literature of the last over two decades, albeit demonstrating inconsistent results ranging from both positive and negative to zero effects and relationships. We are presenting a selective literature representing the varied results. Supporting the positive outcomes, Fonseca, Martí, Redondo, Navarro, and Sánchez (2014) indicated that through the use of technology, students were able to achieve a greater level of direct engagement with the proposed content, which in turn improved overall achievement. They indicated that technology was highly correlated with student motivation, and also found a significant correlation between technology use and academic achievement. In another study Cheng, Lin, and She (2015) found that the students’ long term knowledge retention in a technology enhanced classroom (Virtual Age) subsequently influenced learning outcomes; and students who use technology outperform in engagement and achievement (e.g., Fonseca et al., 2014, Gulek and Demirtas, 2005). Using a longitudinal design, Gulek and Demirtas (2005) provided substantial evidence that using technology enhances student learning and educational outcomes. The findings of the study revealed that compared to non-technology users, students using technology showed significantly higher achievement (overall GPA) and had high scores on criterion referenced standardized tests.

Similarly, Trimmel and Bachmann (2004) found that students who used technology in classrooms reported higher participation rates, more interest in learning, and a greater motivation to perform well as compared to the students who did not use technology. Drain, Grier, and Sun (2012) concluded from the results of their study on high school students that “intelligent use” of electronic devices improves academic performance measured via GPA & standardized test scores; results specifically showed that students who reported spending more time using their electronic devices for academic purposes did better in school than those who claimed they used their devices for other purposes.

In a review of earlier studies of computer and internet use/gaming and its effects on cognitive skills besides some other factors like social skills, relationships, sense of reality and violent behavior among children and adolescents, Subrahmanyam, Greenfield, Kraut, and Gross (2001) found the evidence of some immediate cognitive skills improvement like spatial, iconic, and attentional skills among users of some computer games. However, the researchers recommended more empirical evidence to validate the assumption that long term computer and Internet use (both game and nongame) can lead to long term improvements in cognitive skills and thereby on academic achievement. In another study, Hu and Kuh (2001) used data from 71 four-year colleges and universities in the United States (N = 18,344) and found that using Internet for course material had positive effects on the students’ intellectual development and career preparation, as well as personal development. Similarly, in a study conducted on university students of Pakistan, Suhail and Bargees (2006) found positive effects of Internet use in terms of improved grades and reading, writing and information-processing skills among three quarters of the participants.

Conversely, a number of studies have reported either negative relationship or no significant relationship between technology use and academic performance. Fuchs and Wössmann (2004) surveyed students in 31 countries using a very thorough, detailed survey in order to eliminate other probable causes of the downward inclination of academic performance, stated in their results that the “sheer ubiquity of information technology is getting in the way of learning” (as cited in Ferguson, 2005). Findings from a recent study by Sana, Weston, and Cepeda (2013) suggest that technology use in classrooms has a negative effect on achievement, as measured by performance on a comprehension test. The studies examining the relationship of specific types of technology usage with students’ academic performance have also demonstrated mixed results. For example, Jacobsen and Forste (2011) found a negative correlation between calling, texting, and GPA among university students in the United States. Instant messaging (IM) which today’s college students prefer to use over email (Carnevale, 2006, Horrigan and Rainie, 2005, Junco, 2005) has been studied in relation to academic performance, and results indicate the detrimental effect of IM on school work (Junco & Cotten, 2011); and level of IM use related to academic impairment (Huang & Leung, 2009). Similarly, Lepp et al., 2014, Lepp et al., 2015 found that cell phone use/texting was negatively related to GPA and positively related to anxiety. Previously, Fox, Rosen, and Crawford (2009) have also reported that the time spent on instant messaging during classroom time is negatively correlated with the GPA. Along the similar lines, a number of studies have identified the negative relationship between the frequency of cell phone use and academic performance (e.g., Harman & Sato, 2011) academic difficulty (Hong, Chiu, & Hong, 2012) “intensive” cell phone use and school failure (Sanchez-Martinez & Otero, 2009). Some earlier studies have also reported the negative effect of the internet usage and college students’ academic progress e.g., Malaney (2004–2005) indicated that some students reported that their grades had suffered in consequence of spending too much time on internet. Corroborating these findings, Kubey, Lavin, and Barrows (2001) and Kuh and Hu (2001) found that heavily indulging in online recreation is closely linked to impaired academic performance.

However, Pasek, More, and Hargittai (2009) didn’t report any negative relationship between Facebook use and grades, whereas in the same year Karpinski and Duberstein (2009) had reported a negative correlation between grades and Facebook usage, though the sampling strategy and analytical design of the study was reportedly limited (Pasek et al. 2009). Hunley et al. (2005) found no significant correlation between computer use and grade point average among adolescents. Furthermore, students’ grade point averages (GPA) were not found to be closely correlated with specific activities, such as searching for information, E-mailing, and playing games (link).

Given the fact that current college and university students comprise of a generation of Millennials and even Post-Millennials whose constant interface, competence and confidence with the digital technologies have supposedly led them to develop different learning styles and behavioral characteristics and probably an inherent technological capability of multitasking termed ‘parallel processing functions’ by Prensky (2003), although the proposition is challenged by some critics, and secondly, owing to the affordances these technologies provide, it is intuitively assumed that the usage of these technologies should be influencing their academic performance and outcomes. However, as is evident through the literature the results are confounding and inconsistent. A number of arguments ranging from the contextual and affective to cognitive factors, are presented to elucidate the incongruity of results e.g., some researchers have argued that it is not the quantity of time that students spend online which affects the outcomes rather what they actually do online does matter more vis-à-vis the outcomes (Chen & Tzeng, 2010) and the use of such technologies by students does not necessarily entail that they use them for their academic activities (Bennett et al., 2008, Romero et al., 2011). Along similar lines, Paretta and Cattelano (2013) stated that in-depth observations of students’ technology-based practices suggest them to be sometimes of little academic relevance. Observing 730 individual behaviors of students in the library, the result indicated that though 60% of overall behavior was study related; however, 73% of those working on a computer were significantly more likely to be engaging in a non-study behavior like checking e-mails, visiting Facebook, or other Web sites, etc. In addition, Hong, Hwang, Liu, Ho, & Chen (2014) suggested that ‘cognitive failure’ may also reflect a decrease in the efficiency of perceptual levels of Internet learning. A detailed review of the studies related to explanations of outcomes is beyond the scope of this research paper, hence summing up with Danah Boyd’s (2014) title of the analysis of young people’s uses of digital technology in general, ‘It’s Complicated’, seems quite apposite!

Notwithstanding the diverse research results, it is evident that there is considerable and continued interest in exploring the usage of technology and its outcomes and influences on the academic performance of the college and university students. Most of the research studies have focused on the relationship either between a specific or a couple of technology types and the academic performance, though a few studies have also examined the variety of types of technology. As a matter of fact, today’s youth is utilizing and engrossed in a variety of technology concurrently, an amenity enabled through the modern technology and made accessible anytime anywhere e.g., the mobile phones. Hence, the present study is an attempt towards further explication of the relationships between the usage of the varied types of technology and the academic performance among the four year undergraduate students. Considering the literature outcomes, it is postulated that technology use and academic performance are related. However, in assessing this relationship more comprehensively, a path analysis model will be used.

In addition, for further exposition of the effects of technology use on students’ academic behavior, two other significant and related variables i.e., student engagement and self-directed learning (SDL) will also be examined in the present study.

Student engagement, a broad term that covers physical, academic, and emotional responses, has been the focus of attention of researchers for the last few decades. Student engagement characterizes both the time and energy students dedicate in communications with others through academically purposeful activities (Kuh, 2001). While much has been written on engagement in the classroom and on engagement with technology tools, not much research has been done on the intersection of the two. Research suggests the depth of engagement correlates to the depth of learning, however, an important question is, does technology use contribute to student engagement? There is some research evidence that using technology/social media as an educational tool can lead to increased student engagement (Annetta et al., 2009, Chen et al., 2010, Junco, 2012a, Junco et al., 2011, Patera et al., 2008).

Since technology provides a compelling source of interactive tools for academic purposes ranging from taking notes, participation in discussion forums, access to supplementary resources, software and applications and facilitate student-student and student-faculty interactions, it may foster engagement and self-directed learning (Fried, 2008, Hyden, 2005, Juniu, 2006, Rust et al., 2005, Weaver and Nilson, 2005, White and Robertson, 2015, Williams et al., 2011). Students who use information technology for academic purposes are reported to more likely contribute and participate in active, academic collaboration with other students (Nelson Laird & Kuh, 2005). Promoting a deeper connection between the students, educators, and course content, such partnership specifies that as engagement with technology increases, engagement with academics also increases (Mehdinezhad, 2011). Through the boundless prospects of collaboration, the technology provides, students are enabled to participate in a community of learners resulting in increased accomplishment of learning outcomes, like critical thinking and individual student development, as they become more engaged with the course content (Carini et al., 2006, Kuh, 1993, Kuh, 2009, Kuh et al., 2008, Pike et al., 2011). However, Gosper, Malfory, McKenzie, and Rankine (2011) examined students’ engagement with technologies and explored students’ preferred technologies that support learning, and results indicated that with an exception to social networking, students preferred to use several Web 2.0 tools (emails, learning management systems, YouTube, podcasts) to support their learning at university.

Embedded in the dominion of adult education (Knowles, 1975, Tough, 1971), the concept of Self-Directed Learning (SDL) has been recognized and researched for decades; however, digital revolution has brought it to the forefront and its context has changed with the presence of technology in current learning avenues. Self-directed learning and self-regulated learning are often used interchangeably, however, existing educational theories attempt to bring in conceptual clarity of the two concepts, e.g., Jossberger, Brand-Gruwel, Boshuizen, & Wiel (2010) suggest that the skills of the two concepts ascribe to different levels: the construct of self-directed learning to be situated at the macro level, while self-regulated learning is identified to be at the micro-level. Some theorists have distinguished the two concepts as covert and overt regulatory schemes e.g., Pilling-Cormick & Garrison (2007) view self-directing learning capabilities as the overt management of the external learning environment and self-regulating learning capabilities (SRLC) as the covert management of the internal learning environment (cognitive and affective), analogous to Pintrich’s (2004) concept of SRLC, an intra-individual system.

Notwithstanding the differences and similarities in the two concepts, the interesting and novel communication networks and virtual learning communities accessible through the information and digital technologies have expanded the meaning of lifelong learning (Kim, 2010, Thorpe, 2005) to which both the self-directed learning and self-regulated learning are considered to be the vital tools.

SDL is promoted as one of the critical skills for 21st Century students and the development of SDL skills are much emphasized e.g., Glenn (2000) as cited by Barnes, Marateo, & Ferris (2007) stated, “Net Geners need self-directed learning opportunities, interactive environments, multiple forms of feedback, and assignment choices that use different resources to create personally meaningful learning experiences”. It is argued that technology-rich learning environment can provide students with great opportunities and abilities to be self-directed in their learning as it warrants the students to be not only knowledgeable about the pertinent resource selection, but also the management and appropriate usage of the information (Fahnoe & Mishra, 2013). The self-directed aspects of learning (the choice of what, when, and how long to study) which social media and other technologies provide have significant repercussions in the effectiveness of the user’s learning efforts (Tullis & Benjamin, 2011). It has been suggested that self-directed learning could provide a more direct route into understanding the actual dynamics of and relationships between learning and technologies (Candy, 2004). Despite a significant level of agreement about the influence current technology affordances could have on SDL, as is evident from the review of literature, not much empirical evidence is available regarding the impact of technology use on self-directed learning. The present study aims to address the gap by examining the impact of a variety of technologies on self-directed learning (SDL), in addition to the students’ academic performance and student engagement. Using a path model to test the interrelationships, the study would add an innovative dimension to the existing body of literature.

Based on the findings of previous research representing both positive and negative correlations of technology with the academic performance, and the correlations of technology use with student engagement (e.g., Fonseca et al., 2014, Junco, 2012a), SDL (e.g., Fahnoe & Mishra, 2013) and academic performance (e.g., Hunley et al., 2005), it was hypothesized that: (a) use of various types of technologies will be correlated with the students’ academic performance (GPA), and (b) there will be a positive correlation between: (i) the technology use and Self-Directed Learning (SDL), and (ii) technology use and student engagement. Further, as indicated by the review of studies on student engagement, self-directed learning and academic performance (Carini et al., 2006, Kuh, 1993, Kuh, 2009, Kuh et al., 2008, Nelson Laird and Kuh, 2005, Pike et al., 2011), paths connecting student engagement and SDL, as well as SDL and academic performance were also added to the hypothesized model. The final path model of the study is presented in Fig. 1 (Fig. 1 here).

Section snippets

Participants

The sample comprised a total of 761 female undergraduates enrolled in a private university in Saudi Arabia (M = 20.79 yrs, SD = 1.97). Of those reporting their year in college, freshmen (n = 228) comprised 30%, sophomores (n = 238) 31%, juniors (n = 164) 22%, and seniors (n = 131) 17%. Participants were moderately using media and technology (M = 5.72, SD = 1.58), but 28% (n = 216) of them reported that they did not have a Facebook account. All participants indicated that they use technology

Preliminary analysis

Correlational analysis were used to examine the relationships between students’ technology use, self-direction, and engagement scores. Results indicated that technology use was positively correlated with self-direction, r(759) = 0.46, p < 0.01 and engagement, r(759) = 0.31, p < 0.01. There was a moderate correlation between self-direction and engagement, r(759) = 0.55, p < 0.01. Further, self-direction was also positively correlated with achievement, r(668) = 0.12, p < 0.01. No significant

Discussion

The present study examined the relationship between the technology usage and student engagement, self-directed learning (SDL) and academic achievement among undergraduate university students. Findings of the path analysis demonstrated that technology use predicts self-directed learning (β = 0.32, p < 0.01) and student engagement (β = 0.31, p < 0.01), but has a negligible overall relationship with academic performance (β = −0.08, p = 0.06). However, a perusal of the sub-sets of technology use

Conclusion

For any higher educational institution worldwide, a crucial issue is, does technology improve student learning (Loveless, 1998) and student engagement, and how best to inculcate the self-directed learning skills a.k.a the life long learning skills among students. Using a path model, the present multivariable study provides a unique contribution to the existing literature by exploring the interelationship among a set of critical constructs of academic behaviors central to today’s educational

Author note

The study concept was developed by Rashid and Asghar. Data collection, data analysis, and data interpretation were performed by Asghar in consultation with Rashid. Both Rashid and Asghar did the review of literature and drafted the manuscript. Rashid wrote the final abstract, introduction/review of literature, discussion and conclusion, as well as limitations and future directions, and commented on the manuscript at all stages. Rashid also provided critical revisions and finalized the paper.

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