Amber N. Finn and Andrew M. Ledbetter

19Instructor and Student Technology Use in the College Classroom

Abstract: Technology has become commonplace in the college classroom, offering both opportunities and challenges for instructors. We begin this chapter by examining students’ expectations for instructors’ use of technology for teaching purposes, reviewing some of the most prevalent technologies utilized by instructors currently (i.e., PowerPoint and audience response systems), and identifying best practices for effectively incorporating instructional technologies in the classroom. We then turn our attention to students’ use of their own technological devices (i.e., smartphones, tablets, and laptops) and highlight three contradictions (i.e., social, task, and theoretical) that characterize current research and praxis. Finally, we summarize current research on teachers’ technology policies, pointing out that students desire choice, social regulation, and clarity.

Keywords: instructional technology, teacher technology policies, technology expectations, social influence model, smartphones, tablets, PowerPoint, audience response systems

In this chapter, we review research regarding instructional technology in the classroom. Even a cursory glance at the literature reveals this is no small task. Although smartphones and tablets are relatively new, scholars have long considered how technology influences student learning, including research on instructional uses of television (Guba & Snyder, 1965), overhead projection of transparencies (Boswell, 1980), and graphing calculators (Quesada & Maxwell, 1994), to name just a few technologies of scholarly interest. Nevertheless, the last decade has witnessed widespread proliferation of mobile technologies that students bring into the classroom of their own initiative (Ledbetter & Finn, 2016). This change complicates the technological environment of the classroom considerably and has, understandably, heightened instructors’ concerns about the proper role of technology, especially given evidence that some technologies may distract students and inhibit their learning (Kuznekoff & Titsworth, 2013).

To provide focus, we limit the scope of this review using three criteria. First, this chapter focuses on technologies that enjoy widespread use at the time of writing (i.e., the middle of the 2010s), such as tablets, cell phones, laptops, Power Point, and audience response systems. Although we believe studies of earlier technologies still possess value for both historical and theoretical reasons, clearly newer technologies are of greater interest to most instructors. Second, we focus on research regarding technology use in the college classroom. The robust body of literature on technology use in primary and secondary education is important, but comparing and contrasting it with studies of the college classroom is not the goal of this chapter. Third, despite the growing importance of distance education (Moore, 2013), here we focus on technology use that occurs in traditional face-to-face instruction. Guided by these criteria, the first section of this chapter will consider technology utilized by the instructor, and then we will turn attention to technology use initiated by the student.

Instructors’ Use of Technology

In an effort to increase student learning, college and university instructors have incorporated a wide variety of technologies into classroom instruction. For example, in recent years, instructors have employed YouTube videos (Fleck, Beckman, Sterns, & Hussey, 2014), Twitter (Young, 2009), Skype (Garner & Buckner, 2013), and 3D animation (Perry, Cunningham, & Gamage, 2012), to name but a few. In this section, we will begin by reviewing students’ expectations for instructors’ use of technology in the college classroom. Then, we will review two popular technologies currently being utilized by instructors, namely computer-generated slides (i.e., PowerPoint) and audience response systems (ARS or AR systems). Finally, we will discuss a few practical implications for instructor use of technology based on our current understanding of PowerPoint and AR systems in the college classroom.

Students’ Expectations: Do Students Expect College Instructors To Use Technology?

College students like and expect their instructors to use technology. This can perhaps best be exemplified by looking at a series of studies conducted by Schrodt and his colleagues (Schrodt & Turman, 2005; Schrodt & Witt, 2006; Turman & Schrodt, 2005; Witt & Schrodt, 2006) in which they explored the influence of instructors’ technology use on students’ perceptions of the course and the instructor. They utilized an experimental design with hypothetical scenarios in which they manipulated the amount of technology used by the instructor – no technology, minimal technology, moderate technology, and complete technology. In the no technology condition, the instructor did not use technology in (e.g., no PowerPoint) or outside (e.g., no email) the classroom. In the minimal technology condition, the instructor utilized a limited number of overheads and video clips during class (no PowerPoint) and emailed with students once a day outside of class. The moderate technology condition involved the instructor using PowerPoint, video clips and web resources in class, and the instructor frequently used e-mail, had virtual office hours, used an online chat room, and required students to turn in assignments via e-mail. Finally, in the complete technology condition, the instructor explained that the first day would be the only face-to-face meeting. The course would be delivered over the Web via recorded video lectures, online handouts, online exams and assignments, chat rooms and other web resources. The scholars consistently found that students expected their instructors to use minimal to moderate amounts of technology. When they did, students perceived them to be more credible and students reported higher affect for the course and instructor (Schrodt & Witt, 2006; Witt & Schrodt, 2006). Further, immediacy interacted with technology use such that highly immediate instructors who used minimal to moderate amounts of technology received the highest ratings on teacher credibility and affect (Schrodt & Witt, 2006; Witt & Schrodt, 2006). Although it is unclear from this line of research how technology utilized during class vs. outside the classroom differentially impacted students’ perceptions, it does clearly suggest that students enter the classroom wanting and expecting their instructors to use at least some form of technology to enhance teaching and learning.

Types of Technology: What Types of Technology Are Instructors Currently Using?

Given students’ desire and expectation for technology, the question then becomes – what types of technology are instructors currently employing in the classroom? As previously noted, instructors are incorporating a variety of technologies into the learning process. However, as we surveyed the literature, computer-generated slides (i.e., PowerPoint) and audience response (AR) systems appeared to be the most widely utilized and generally accepted technologies being implemented in the classroom today. Although there is an extensive body of literature pertaining to each type, below we highlight student perceptions of the technologies and associated learning outcomes.

PowerPoint

Since its introduction in 1987, PowerPoint has gained prominence in the academy, and it is conceivably the most common type of technology currently being utilized by instructors in the college classroom. An extensive body of research conducted in the United States, the Netherlands, and the United Kingdom indicates that students have a strong preference for, value, and expect their instructors to use Power Point when teaching (Atkins-Sayre, Hopkins, Mohundro, & Sayre, 1998; Bartsch & Cobern, 2003; Beets & Lobingier, 2001; Blokzijl & Naeff, 2004; Daniels, 1999; Hill, Arford, Lubitow, & Smollin, 2012; Levasseur & Sawyer, 2006; Savoy, Proctor, & Salvendy, 2009; Susskind, 2005, 2008; Szabo & Hastings, 2000). More specifically, students prefer PowerPoint to traditional lectures utilizing overhead transparencies (Perry & Perry, 1998; Susskind, 2008; Szabo & Hastings, 2000) and the chalk or whiteboard (Beets & Lobingier, 2001; Perry & Perry, 1998; Susskind, 2005) and perceive PowerPoint to be more attention-grabbing, interesting, motivating, and beneficial to learning (Szabo & Hastings, 2000). Students report that their notes are more organized, easier to understand, and useful for studying (Susskind, 2008). They believe PowerPoint improves learning and their overall experience in the classroom by aiding in exam preparation, enhancing comprehension of course material, improving attention in class, and making class more interesting (Hill et al., 2012). Further, when PowerPoint is used, students have enhanced perceptions of the teacher and report being more motivated to attend class, leaving Susskind (2008) to conclude, “Computer-mediated presentations may create an overall positive impression that students rely upon when forming impressions of the instructor and course-related issues” (p. 1235).

Despite students’ overwhelmingly positive affect for computer-generated lecture slides, students and instructors have acknowledged some of the drawbacks of PowerPoint. For example, Hill et al. (2012), using survey data from faculty and students at a private university in New England, raised three dilemmas or tensions associated with instructional use of PowerPoint. First, they suggested a tension between clarification and oversimplification. That is, instructors and students both liked PowerPoint’s ability to organize and simplify course material, but instructors were concerned that synthesizing complex information into bulleted lists diluted knowledge and hampered learning. The second dilemma they identified was between entertaining and education. Students reported that PowerPoint captured their interest and helped them pay more attention; however, students and instructors indicated that the technology resulted in mindless copying of notes and kept students from participating in the class discussion. Instructors were particularly concerned that PowerPoint inhibited interactive teaching, fostered passivity among students, and distanced them from their students. Finally, the scholars highlighted a tension between career pragmatism and pedagogical commitment. Although the instructors in the study questioned the pedagogical merit of PowerPoint, they reported continued use because students liked and expected it, and they were concerned about their own teaching evaluations being negatively impacted if they chose not to use it. Other research has pointed to similar concerns (James, Burke, & Hutchins, 2006; Szabo & Hastings, 2000).

Additionally, it is unclear how, if at all, instructors’ use of PowerPoint impacts student cognitive learning. In Levasseur and Sawyer’s (2006) comprehensive review of the effects of PowerPoint in the classroom, they concluded, “The majority of extant studies have found no significant change in learning outcomes when instructors augment their lectures with computer-generated slides” (p. 111). They go on to explain that two of the studies showing enhanced cognitive learning (Jensen & Sandlin, 1992; Szabo & Hastings, 2000, study II) had serious methodological limitations, and the four others that found moderate increases in learning (Lowry,1999; Mantei, 2000; Weinraub, 1998; Wilmoth & Wybraniec, 1998) provided students copies of the course PowerPoint slides. They thus posited that the contradiction in the literature could be a result of some students having “copies of a thorough and organized set of class notes” (Levasseur & Sawyer, 2006, p. 112). However, in a follow-up study almost ten years later, Worthington and Levasseur (2015) found that access to instructor-provided (IP) lecture slides did not lead to enhanced learning, and in fact, bringing course slides to class to use during lecture resulted in less rather than more learning. Consequently, they argued that “Slides are actually altering students’ in-class experience in some meaningful way. More specifically, when IP slides become a part of the note taking process, students may simply become … more passive in the learning process” (Worthington & Levasseur, 2015, p. 21).

This leads to the question, “What explains the current discrepancy between studies that result in enhanced cognitive learning and those that do not?” Although additional research is still needed, Levasseur and Sawyer (2006) speculated that how educators utilize the technology, individual student differences (e.g., learning styles), and slide construction may help explain the divergent results. Consequently, they pointed to previous research such as Bartsch and Cobern’s (2003) study, which examined three types of PowerPoint slides – text only, text and relevant picture, and text with non-relevant picture. In their study, students liked and recalled more from the text only and text with related graphics conditions than they did the text with unrelated graphics conditions. Further, the scholars found that graphics were not needed for teaching simple declarative information, but suggested it may be beneficial when difficult, complex, or abstract concepts are being taught. Surprisingly though, besides the few studies such as this that have begun to examine slide construction, little research has focused on how instructors are using PowerPoint (i.e., method or pedagogy) or on how teacher communication behaviors influence the relationship between use of the technology (i.e., Power Point) and learning outcomes. Clark (1983) argues that scholars must look to the instructional method, not the media, to determine differences in learning performance, and Schrodt and Witt (2006) suggest that scholars need to consider instructor communication behaviors and instructional technology use concurrently to fully understand the role of technology in the learning process. Thus, to determine the relationship between instructional use of PowerPoint and learning outcomes, especially cognitive learning, as Susskind (2005) so aptly stated, “It may be time to conduct more fine-grained analyses rather than only assessing whether the presence or absence of multimedia influences performance” (p. 212).

Overall then, students have positive affect for PowerPoint in the classroom. However, instructors, and to a lesser extent students, have expressed some concern with instructors’ use of PowerPoint, including the notion that PowerPoint may hinder learning. Additional research is needed to determine the exact relationship between PowerPoint and cognitive learning.

Audience response (AR) systems

Dating back to the 1960s, college and university instructors have utilized AR systems in the classroom, but marked technological advancements and pedagogical shifts have incited their use in recent years, making them one of the most prevalent technologies employed by instructors today (Caldwell, 2007; Judson & Sawada, 2002; Kay & LeSage, 2009). AR systems allow instructors to project questions, usually multiple-choice questions, onto a screen and students respond to the questions using an electronic handheld device, thus allowing instructors to get instant feedback from students regarding students’ understanding of course content. Upon last count, over 26 different labels have been used to identify these systems (Kay & LeSage, 2009), including electronic response system (ERS), personal response systems (PRS), student response systems (SRS), personal voting systems (PVS), and clickers, to name but a few. Therefore, from here on, we will use AR system or ARS to refer to this technology. Additionally, there are numerous comprehensive literature reviews on the topic (Caldwell, 2007; Judson & Sawada, 2002; Kay & LeSage, 2009; Simpson & Oliver, 2007), which we will refer to throughout this section. Our intention is not to repeat them, but rather to use them to aid in our discussion of student perceptions of AR systems and associated learning outcomes.

AR systems are typically comprised of four elements: (1) a handheld remote device for each student (or sometimes groups of students), (2) a receiver to capture responses, (3) software for embedding and displaying questions, and (4) hardware to present the questions and answers, which is usually a computer and projector (Jefferies, Cubric, & Russell, 2013). Over the years, technological advancements have led to portable and wireless systems, advanced administrative record-keeping capabilities, and rapid tabulation and display, usually in the form of a histogram, of all students’ answers (Judson & Sawada, 2002). Additionally, in line with the BYOD (Bring Your Own Device) movement in higher education (Johnson, Adams-Becker, Estrada, & Freeman, 2015), emergent technologies now allow students to use their own mobile devices (e.g., phone, laptop, tablet) to respond to the instructor’s questions. Recent research conducted by Stowell (2015) indicates that AR systems (i.e., clickers) and mobile devices can be used concurrently in the classroom, but mobile devices can sometimes be less reliable than AR systems that do not depend on internet-based technology. However, the type of technology (i.e., mobile device or ARS device) students choose to use does not seem to impact students’ overall course performance (Stowell, 2015). Additionally, Wang’s (2015) research shows how students can use their own devices to participate in game-based student response systems.

As we previously observed with PowerPoint, students in the United States, Hong Kong, and Canada have consistently reported overwhelmingly positive perceptions when instructors implement AR systems in the classroom (Caldwell, 2007; Denker, 2013; Han & Finkelstein, 2013; Judson & Sawada, 2002). In an extensive review, Caldwell (2007) concluded that students find AR systems to be “stimulating, revealing, motivating, and – as an added benefit – just plain fun” (p. 19). Similarly, in a more recent study, Denker (2013) notes that when AR systems are used, students report enhanced enjoyment, engagement, motivation, participation, and affect for the course and instructor. Most recently, Stowell (2015) found that students reported enhanced involvement, stimulation, and enjoyment when AR systems were employed, regardless of the type of technological device students chose to use to respond to the instructor’s questions, and in Wang’s (2015) study, students in Norway reported being engaged and motivated when a game-based student response system was implemented in the course, even when the instructor used it during every lecture.

However, it should be noted that AR systems are not without critique. Some students have reported minimal dissatisfaction with the technology. Common complaints include the cost of purchasing the device or a polling subscription (when mobile devices are used), reliability of internet-based technology, technical problems, and clarity of questions (Caldwell, 2007; Kay & LeSage, 2009; Stowell, 2015). Nonetheless, students by and large report positive affect for AR systems, and instructors seem to have similar positive perceptions (Caldwell, 2007; Judson & Sawada, 2002; Kay & LeSage, 2009).

Research conducted by Denker (2013) indicates that both instructor communication behaviors and student traits impact students’ perceptions of AR systems. Specifically, highly empowered students reported being more engaged and learning more from AR systems than did less empowered students. Additionally, when students perceived their instructor to be immediate, they reported increased learning and engagement from AR systems. Denker concluded, “This reinforces the fact that technology is a tool in the service of pedagogy and not pedagogy in and of itself … instructors’ performance is very important in shaping how students will perceive their engagement and learning, even if it is mediated” (p. 61). On a related note, Han and Finkelstein (2013) found a positive relationship between the amount of AR system training an instructor received and students’ perceived engagement and learning.

In addition to students enjoying and valuing instructors’ use of AR systems in the classroom, AR systems are associated with several other positive learning outcomes. In their comprehensive review, Kay and LeSage (2009) identified three categories of benefits supported by empirical research – classroom environment benefits, learning benefits, and assessment benefits. Classroom environment benefits included improved attendance, especially when grades were associated with ARS use, enhanced student attentiveness during class, and increased student participation during class due to the anonymity of responses. Learning benefits involved frequent and positive student-to-student interactions, increased quantity and quality of discussion, more contingent teaching, improved learning performance, especially in comparison to traditional lectures, and enhanced depth of learning. Finally, assessment benefits included improvements in the feedback process and enhanced effectiveness of formative feedback.

In terms of the learning performance benefits referred to by Kay and LeSage (2009), research suggests that AR systems enhance cognitive learning when used in conjunction with constructivist pedagogy. In their extensive review, Judson and Sawada (2002) explained that when AR systems were first implemented in the college classroom in the 1960s and 1970s, instructors operated under a behaviorist pedagogy in which the instructor projected a multiple-choice question, students responded, and the instructor assessed understanding. If needed, the instructor provided additional information before moving on with the lecture. When AR systems were used in this way, scholars found no gains in student achievement measured by standard exams. Judson and Sawada went on to explain that although some instructors still utilize the stimulus-response style employed in early research, most instructors and scholars now work under a constructivist pedagogy and promote student interaction and collaborative discourse. They described one method of doing this, referred to as Classtalk, which involved the instructor displaying a question and students having collaborative discussion with other students prior to submitting their answer via their handheld device. Then, the instructor displayed a histogram of responses, which served as a catalyst for additional collaborative discourse. Judson and Sawada indicated that when instructors operated under a constructivist pedagogy, scholars found significant gains in academic achievement. Accordingly, it is the pedagogical practices of the instructor, not the presence or absence of the technology, that helps explain the relationship between AR systems and cognitive learning.

Recently, in an effort to better understand the positive association between AR systems and learning, Blasco-Arcas, Buil, Hernandez-Ortega, and Sese (2013) examined a model in which they proposed that the amount of interactivity with peers and with the teacher while using AR systems influences students’ perceived active collaborative learning and perceived engagement, which in turn, impacts perceived learning performance. They empirically tested the model using survey data from undergraduate students enrolled in business classes in Spain. They found strong support for their framework.

Overall then, students and instructors have overwhelmingly positive affect for AR systems. AR systems are well received by students, and if used correctly, they can lead to numerous positive learning outcomes. Most notably, AR systems have the potential to enhance cognitive learning when instructors operate under constructivist pedagogy and allow students to participate in collaborative discourse while using the AR system.

Practical Implications for Instructor Use of Technology

Although PowerPoint and AR systems are only two of the countless number of technologies instructors can implement in the classroom, research on these tools provides important practical implications for those interested in effectively incorporating any type of technology into the higher education classroom.

Student-Initiated Use of Technology

In decades past, if electronic/ digital technology appeared in the classroom, it was provided by the teacher. However, especially since the widespread adoption of laptop computers, cell phones, and tablets in the early 21st century, students increasingly bring their own devices into the classroom. Although some students may use such technology for instructional purposes, students frequently employ it for offtask purposes (Baker, Lusk, & Neuhauser, 2012; Williams et al., 2011); even well-intentioned students may lack the self-regulation needed to resist the temptation to check social networking sites, play video games, or text with friends (Zhang, 2015).

In this section, we will consider student-initiated use of technology. As with the rest of this chapter, we will confine our discussion to use that occurs (a) in the college classroom (versus outside it), (b) in the present day (versus an historical approach), and (c) using the specific wireless technologies that are currently popular yet controversial (i.e., laptops, tablets, and cell phones). As we survey the literature, we observe three contradictions that characterize research and praxis regarding students’ classroom use of communication technology. First, we note a social contradiction, such that students feel social pressure to use technology for non-relevant purposes in class, even though they also believe such use is socially inappropriate. Second, students want access to technology to aid their learning, but most studies to date reveal nonsignificant or harmful effects; this is a task contradiction. These first two contradictions concern the student experience. The third contradiction concerns researchers: Although instructional communication scholarship has demonstrated that students are more than processors of information, almost all extant research on student use of technology ignores all other facets of the students’ identity and role; this is a theoretical contradiction. We will consider each of these contradictions in turn.

Social Contradiction: Social Pressure to be Socially Inappropriate

With astounding rapidity, mobile technology has achieved nearly ubiquitous market penetration, with frequent use in both industrialized and developing nations (James, 2014). That ubiquity has produced both the ability to keep in constant contact with social network members and, perhaps more important, the expectation that people will do so (Campbell & Park, 2008). Most germane to our discussion here, Hall and Baym (2012) documented this expectation of continual contact among college students, finding that frequent cell phone communication may, in some cases, lead to feelings of entrapment (i.e., inability to achieve freedom from the social network when desired) and overdependence (i.e., feeling too strongly connected to a friend). It makes sense, therefore, that students who send text messages during class tend to have large social networks, and in some cases, intrusive thoughts about technology may drive their habitual use (Olmsted & Terry, 2014).

In light of this social pressure, as well as students’ frequent technology use outside the classroom (Wentworth & Middleton, 2014) and multitasking during such use (Judd, 2015), it is unsurprising, then, that college students expect to have access to their wireless devices in the classroom. Building from Fulk’s (1993; Fulk, Schmitz, & Ryu, 1995) social influence model, we have contended that this expectation socially constructs their perception of and response to instructors’ attempts to regulate student use of technology (Finn & Ledbetter, 2013; Ledbetter & Finn, 2013). When students do have access to communication technology in the classroom, several studies have demonstrated that they frequently use it for non-course related purposes, such as maintaining contact with peers (Skolnik & Puzo, 2008; Tindell & Bohlander, 2012). Case in point, Baker et al.’s (2012) study of nearly 900 students found that 40% of students send at least one text message in every class session they attend; beyond social uses, students may also turn to their wireless devices to alleviate boredom (Clayson & Haley, 2012), play games (McCoy, 2013), or do work for other classes (Skolnik & Puzo, 2008). However, tendency to use technology may also depend on the student’s cultural background, with one Australian study finding that international students engaged in greater non-course-relevant technology use than did domestic students (Barry, Murphy, & Drew, 2015).

Given that students live in a culture that places a premium on technology (Hall & Baym, 2012) – and that the majority of students indeed use their mobile technology frequently, including in the classroom (Tindell & Bohlander, 2012) – it would stand to reason that the majority of students believe it is acceptable to use technology for non-course purposes during class. Paradoxically, however, research has demonstrated this is not the case; rather, many students seem to feel guilty about such use and experience annoyance when their peers do so. Regarding the former, Williams et al. (2011) observed, “While 79% of the students responded that they text in class, 73% stated that texting in class is unprofessional” (p. 52); on the latter point, Fried (2008) found that students reported other students’ laptop use as the greatest distraction in the classroom. Thus, rather than a laissez-faire approach to classroom technology policies, many students want teachers to create and enforce policies restricting off-task use of communication technology in the classroom (Campbell, 2006; Finn & Ledbetter, 2014; McCoy, 2013). We will consider the nature of such policies later in this review.

Taken together, then, this line of research presents an odd contradiction: On one hand, students experience social pressure to maintain contact with peers during class, yet they simultaneously believe such use is socially inappropriate, especially if it disturbs their classmates. Some students may reconcile these conflicting beliefs by engaging in technology use as surreptitiously as possible (e.g., glancing down at a cell phone hidden in a pocket; Hassoun, 2014). Although some instructors may view such behavior as deceptive or manipulative, perhaps students simply are trying to navigate social norms and expectations that they find unclear and contradictory (Aagaard, 2015; Barry et al., 2015). Future research might advance understanding of these contradictory social impulses (and instructor praxis in navigating them) by appealing to broader communication technology models focused on social construction of meaning. For example, the social influence model (Fulk, 1993; Fulk et al., 1995) recognizes that a person’s social and organizational groups influence beliefs about technology and patterns of use, and the dual-capacity model (Sitkin, Sutcliffe, & Barrios-Choplin, 1992) focuses attention on how receivers interpret the symbolic meaning of technology. Identifying the extent to which students perceive cognitive dissonance between their technology beliefs and technology use may help instructors develop persuasive arguments regarding appropriate use of technology in the classroom (cf. Lancaster & Goodboy, 2015).

Task Contradiction: Wanting Learning Technology that Harms Learning

Fortunately, social contact and entertainment account for only part of the reason college students want access to technology in the classroom. Many students also believe technologies such as laptop computers (Elwood, Changchit, & Cutshall, 2006; Skolnik & Puzo, 2008) and social media (Barczyk & Duncan, 2013) help them learn more effectively. Although students’ embrace of wireless technology is not universal (Lohnes & Kinzer, 2007), many believe access to laptops, at least, enhances their classroom experience (Barak, Lipson, & Lerman, 2006).

The empirical evidence, however, does not accord with students’ optimistic view of technology and learning. Study after study – including both surveys and experiments – have demonstrated an inverse association between various measures of learning and student wireless technology use. The literature that has examined this topic is surprisingly copious given the recent introduction of these technologies to the college classroom. To exemplify this literature briefly, in-class texting is associated with lower grades (Clayson & Haley, 2013; Ellis, Daniels, & Jauregui, 2010), worse note-taking, and diminished memory recall (Kuznekoff & Titsworth, 2013); online chatting also decreases quality of notetaking (Wei, Wang, & Fass, 2014); students who do not use laptops in class learn more than those who do (Helmbrooke & Gay, 2003), perhaps because laptop multitasking leads to diminished learning (Zhang, 2015); honors cohorts with laptop access did not learn more than a cohort without laptop access, although the former were less satisfied with their education (Wurst, Smarkola, & Gaffney, 2008); and when cell phones ring in class, students learn less (End, Worthman, Mathews, & Wetterau, 2010). Moreover, the negative effect on learning does not end with the user. In one experiment, students in view of another student’s off-task use of technology also obtained lower test scores than students not in view of such use (Sana, Weston, & Cepeda, 2013). Although research has demonstrated a clear inverse association between technology use and cognitive learning, it has not always differentiated effectively between instructionally relevant and non-relevant use, and that distinction may matter (Finn & Ledbetter, 2014). For example, Kuznekoff, Munz, and Titsworth (2015) recently demonstrated that course-relevant mediated messages may not diminish learning outcomes. Additionally, other scholars have noted the enhanced learning that can occur when technological devices are used effectively (Barak, Lipson, & Lerman, 2006; Bui, Myerson, & Hale, 2012).

Collectively, studies in this line of research operate from a cognitive/psychological framework focused on efficacy of information processing. Theoretically, they conceptualize the learner as a computer-like brain that possesses a limited amount of informational processing bandwidth. Like a computer with too many applications open, off-task activities drain computational power, inhibiting a students’ ability to focus on difficult material, comprehend it, and encode it in longterm memory (Kraushaar & Novak, 2010). Thus, this approach conceptualizes any sort of off-task technology use as a distraction to the learning process (Wood et al., 2012). If students recognize this threat to their learning, they may engage in self-regulating behavior, or activities designed to curtail or eliminate the temptation to use technology for off-task purposes. For example, some students may close a laptop lid at key moments during a lecture, others may turn off their cell phone, and still others may simply exercise willpower by refraining from non-relevant use of their devices (cf. Park, 2014). Such self-regulating behavior seems effective in reducing the threat to learning, with one study finding that diminished text messaging frequency mediated the association between self-regulating behavior and cognitive learning (Wei, Wang, & Klausner, 2012); heightened self-regulating behavior also mitigates against students’ laptop multitasking (Zhang, 2015).

It is challenging to reconcile these findings regarding technology use and learning with students’ belief that technology aids learning. One study of Nigerian students runs counter to the negative trend, finding that technology use variables were unassociated with GPA (Olufadi, 2015); perhaps the cultural context of technology use differs between developing and industrialized nations, or perhaps technology use does not predict overall GPA as it does specific course grades (cf. Clayson & Haley, 2013). A cynic might conclude that, at best, students are simply naïve about how negative effects of technology outweigh learning outcomes, and at worst, students appeal to educational purposes as a sleight-of-hand to conceal their appetite for distraction. Without diminishing the possible accuracy of these viewpoints, in the next section we consider an alternative: Perhaps this seeming task contradiction indicates, to some extent, a theoretical contradiction regarding the role and identity of the student.

Theoretical Contradiction: Students Use Computers, but Students Are Not Computers

Thus far in this section, we have considered two seeming contradictions regarding student use of technology: (a) students feel social pressure to use technology when it is socially inappropriate to do so, and (b) students want access to technology in order to aid their learning even though access may harm their learning. The student is the locus of these two contradictions. In contrast, our final contradiction focuses on the theoretical approach adopted by scholars in this line of inquiry. The dominant line of theoretical thinking conceptualizes students as, primarily, simple processors of information. Akin to the classic Shannon and Weaver (1949) mathematical model of communication, this approach assumes education is a process of transmitting information from a source to a receiver, and, when students choose to multitask, they introduce noise that interferes with the transmission. Although this model may possess during the past three decades communication scholars have moved away from such transmission models, and the field of instructional communication is no exception to this trend (Preiss & Wheeless, 2014). More pragmatically, many schools and professors have advocated for more community-oriented approaches to learning (e.g., the constructivist classroom, Wurst et al., 2008; flipped classrooms, Tucker, 2012). To keep up with these metatheoretical and pragmatic trends, scholars interested in classroom technology should consider conceptualizing the student as more than an information processor.

Some scholarship already has begun to explore the consequences of such alternative conceptualizations. For example, Aagaard (2015) critiqued the information processing approach from a post-phenomenological perspective, questioning the extent to which students are truly separate from the technology they employ. He contended that users of any technology develop relational strategies toward that technology; for example, with a computer, “a novice is forced to concentrate on each individual keystroke, whereas a skilled user barely notices the computer itself but rather focuses on its contents” (Aagaard, 2015, p. 91). Over time, as students become accustomed to wireless technology, it becomes what he terms a habitual distraction, or a “deeply sedimented relational [strategy]” that is “explainable neither in terms of mental choices nor mechanical reactions to stimuli” (Aagaard, 2015, p. 95). With this theoretical turn, he rejected the view that technology is a tool used to relieve boredom (cf. McCoy, 2013), instead contending that the incorporation of the device into the student’s identity produces mediated impatience: “When a lesson is experienced as boring, this may to a certain extent be because technological alternatives are constantly available and ready to be utilized at a whim” (Aagaard, 2015, p. 95). Relatedly, and also in counter to the information processing approach, Hassoun (2014) de-emphasized the extent to which multitasking influences course grades, instead conceptualizing technology use as a mutually-enacted performance by students and the instructor. This theoretical move encourages the instructor to think about how to enter that performance in a way that will encourage instructional goals; e.g., “Fostering more participatory environments may encourage more students to direct their attentions toward class” (Hassoun, 2014, p. 14).

Beyond these cultural and identity approaches, scholarship may also advance by considering students’ emotions as well as their intellect. Instructional communication scholarship is well-positioned for this conceptual turn, having long recognized a distinction between cognitive learning and affective learning, or a student’s state motivation to learn course material and pursue study of it in the future (Anderson, 1979; Bloom, 1956). Whereas cognitive learning has received sustained attention in the literature on student technology thus far, almost no research has considered the effect of technology on affective learning. This is unfortunate, given that affective learning serves as a mediator between the teacher’s behaviors and cognitive learning (Allen, Witt, & Wheeless, 2006; Rodriguez, Plax, & Kearney, 1996); stated less technically, students are less likely to learn with their minds unless they are emotionally engaged with the course. Instructional messages through communication technology may be one method of generating students’ emotional engagement with the instructor and, perhaps, the course content (Mazer, Murphy, & Simonds, 2007).

In a recent study we investigated the extent to which emotional/motivational factors may foster or inhibit off-task use in the college classroom (Ledbetter & Finn, 2016). Building from prior work in the uses and gratifications theory tradition (e.g.,Leung & Wei, 2000), we discovered two distinct factors of non-course use: (a) alleviating loneliness, or internally-motivated use to reduce an aversive emotional state, and (b) relational maintenance use, or externally-motivated use to keep in constant contact with peers. Each factor possessed distinct predictors, suggesting that non-course use may not be as monolithic as some previous research has conceptualized it. For alleviating loneliness use, we found a curvilinear effect for learner empowerment, such that use was highest for students at moderate levels of the variable and comparatively less use for both highly empowered and unempowered students. Perhaps highly motivated students eschew technology use to engage with the course, whereas unmotivated students limit technology use at least for the sake of obtaining a passing course grade; the student with an average level of motivation, who is doing just enough to get by, may be most drawn to communication technology to alleviate a sense of emotional ambivalence about the course (Aagaard, 2015; McCoy, 2013). In contrast, frequency of relational maintenance use was a product of both students’ emotions (i.e., learner empowerment) regarding the course and their attitude toward the technology. For students who did not particularly enjoy communication technology, learner empowerment predicted reduced non-course use; for those who did enjoy communication technology, learner empowerment predicted greater relational maintenance use. Although our study operated from a different theoretical and methodological approach than Hassoun (2014), these results accord with his contention that some student technology use is an exercise in habitual distraction, even for students who possess strong motivation to engage with a course.

Viewed overall, the extant literature on student use of technology in the classroom demonstrates a theoretical contradiction: Many studies treat students as information processors, even though that is, at best, just one component of the student’s role (and instructor’s goal). Scholars can resolve this contradiction by applying alternative conceptualizations to the student’s use of technology. Such work may require qualitative scholarship to inductively derive new aspects of the relationships among students, technologies, instructors, and contexts. As for quantitative scholarship, researchers should explore beyond the cognitive learning variables frequently studied so far. This is a theoretically exigent need not only for the sake of the comprehensiveness of our theoretical understanding, but also for shifting application and theory to collectivistic cultural contexts, where conceptualization of the student as an individualized information processor may contradict cultural assumptions and, moreover, teachers’ compliance-gaining strategies regarding technology use may differ from individualistic instructors (Lu, 1997).

Teachers’ Classroom Technology Policies

As we have discussed throughout this chapter, technology offers both opportunities and challenges for instructors. Today’s instructors must make important decisions regarding how they and (particularly) their students will use technology in the classroom. In hopes of helping instructors make informed decisions and advancing our theoretical understanding of technology and the learning process, we have conducted a series of studies (Finn & Ledbetter, 2013, 2014; Ledbetter & Finn, 2013) examining teachers’ technology policies or “the rules governing the use of wireless communication technologies in the classroom” (Finn & Ledbetter, 2013, p. 27). In this section, we will highlight five key discoveries from our research.

First, in line with what we discussed previously regarding students wanting and expecting their instructors to use technology to teach course content and following Fulk’s (1993; Fulk et al., 1995) social influence model, students expect to be able to use their own wireless devices (e.g., phones, laptops, tablets, etc.) for academic purposes in the classroom. We found that when instructors encouraged students to use their own devices for course and learning purposes, students perceived the instructor to be more credible (Finn & Ledbetter, 2013), and students felt that the course was more meaningful and that their participation in the course made a difference (Ledbetter & Finn, 2013). However, contrary to this finding, in our most recent study (Finn & Ledbetter, 2014), students perceived the instructor to be more verbally aggressive when he or she encouraged or required students to use their wireless devices for educational reasons. We speculated that students may very well want a choice when it comes to how, if at all, they are going to use their wireless communication devices for academic purposes. In other words, students do not want their instructors to tell them when and how to use their devices to enhance learning; instead, they want to decide for themselves if they need to use a device to augment their own individual learning. Thus, it seems students want to be able to use their wireless communication devices for academic purposes if and when they feel it is necessary.

Second, our research has consistently indicated that students want and expect instructors to regulate technology use for social purposes. That is, students expect instructors to prevent wireless devices from interfering with the teaching and learning that should be occurring in the classroom. For example, when instructors utilized technology policies that discouraged use of wireless devices for social purposes during class, students perceived them to be less verbally aggressive (Finn & Ledbetter, 2014). Further corroborating this, we found that students are more sensitive to the regulation of devices they perceive to be more instructionally relevant (Finn & Ledbetter, 2014). Specifically, when instructors employed technology policies that regulated laptop usage, students perceived the instructor to be more verbally aggressive; however, this was not the case when the instructor regulated cell phone usage.

Third, not only do students expect to be able to use their wireless devices if and when they want to for academic purposes and for instructors to regulate usage for social purposes, they also expect instructors to have very clear technology policies. We found that when instructors employed laissez-faire technology policies, or acted in ways that suggested they did not care how students used technology during class, students perceived the instructors were not drawing from the expert and referent power bases when enforcing policies and procedures in the classroom, and this in turn had a negative impact on students’ perceptions of the teachers’ credibility (Finn & Ledbetter, 2013). Additionally, students reported finding the course less interesting and valuable when the instructor moderately discouraged noncourse-relevant technology use than when the instructor either strongly or minimally discouraged it. We consequently concluded, “Perhaps what is most important regarding technology policies is that the teacher has explicit rules regarding the use of technology in the classroom, clearly communicates the rules, and consistently enforces the rules” (Ledbetter & Finn, 2013, p. 312).

Fourth, in line with Schrodt and Witt’s (2006) recommendation, to fully understand the role of technology in the learning process, scholars need to consider teachers’ communication behaviors and technology policies concurrently. Our research has repeatedly shown that teacher communication behaviors mediate the relationship between teacher technology policies and learning outcomes. For example, we found that students’ perceptions of instructors’ technology policies influenced instructors’ use of prosocial and antisocial power bases, which in turn, predicted teacher credibility (Finn & Ledbetter, 2013). This was important in showing that an instructor could violate students’ technology expectations and still be perceived as competent if he/she drew from the reward and expert power bases (Finn & Ledbetter, 2013). Thus, it is the combination of the policy and the way the teacher communicates in the classroom that influences students’ perceptions and learning outcomes.

Finally, like teacher communication behaviors, students’ characteristics and traits also play a role in the learning process and need to be taken into consideration when examining the relationship between technology policies and learning outcomes. For example, we found that students’ apprehension to communicate online moderated the relationship between discouraging policies and the competence dimension of learner empowerment. This was important in highlighting that highly apprehensive students in particular feel less competent when instructors have unclear technology policies for regulating usage of wireless communication devices in the classroom.

It should be noted though that, to date, our research on teacher technology policies has all occurred at a private university in the southwestern United States. Additional research is needed to determine the extent to which our results generalize to other populations.

Conclusion: Expectations and Choices

Although it is difficult to speak across a body of literature as vast as that on technology use in the classroom, we nevertheless observe two theoretical threads that run throughout much of this scholarship. First, several studies examine (or at least acknowledge) students’ and instructors’ expectations regarding technology use. The clearest and most theoretically grounded example of this is the work of Schrodt and Witt (2006), which invoked expectancy violations theory (Burgoon & Le Poire, 1993) in order to explain students’ expectations for frequency of technology use. However, students are not the only classroom stakeholders who possess expectations about technology; instructors do as well, and exploring the extent to which those expectations influence technology policies (and enforcement of those policies) is one direction for future research. Although longitudinal studies are difficult to execute, they may be particularly helpful here, as expectations for technology use are not static over time.

Second, we note that choice represents a fundamental question in the literature to date. This makes sense, given that students and instructors make decisions about the latitude of technology use they will engage in or allow. Without denying the importance of choice, instructional scholarship may benefit from reflection on the extent to which technology use is beyond student or instructor control. This question hearkens to an old debate within the philosophy of technology: does technology shape society, or does society shape technology? Although philosophers of science have answered this question in diverse ways, Hughes (1996) contended that social forces shape use and policy more strongly in the early days of a technology; once the technology reaches maturity and deep integration with social structures, technology becomes a fixture of the social system that requires massive effort to change. Case in point, electric lighting has been part of instruction for a relatively short time in the history of the university, yet it would be difficult for an instructor to curtail its use today (the beauty of a sunny day on the campus lawn notwithstanding). Instructors and researchers are likely aware of choice when considering the trendiest technologies-of-the-moment, but these change with each passing year, and the eventual deep integration of technologies may blind scholars to their effects. Situating future research within the level of integration achieved by a technology, and thus the perceived degree of choice regarding use, may prevent studies from becoming outdated as old technologies mature and new technologies inevitably appear.

Indeed, it is the rapid pace of change that makes classroom technology so exciting, yet also so challenging. New technologies excite instructors with their possibilities for learning, yet present challenges regarding effective application of it. Students excitedly bring new technology into the classroom but do not reflect carefully on how to balance their desire for connectedness with their desire to learn. Researchers seize on innovative technology topics, yet struggle to connect their findings to broader theoretical concerns. We likewise expect that the technologies we have addressed in this review will appear outdated to readers just a decade or two into the future, but we also expect that ongoing practical and theoretical questions about learning, expectation, distraction, and choice (among others) will remain. We encourage scholars of instructional communication technology to strive to connect their research to these enduring questions.

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