The Digital Culture: Potential Effects on Creative and Higher Order Thinking.
The importance of educating students to think critically and creatively was recognized over 2000 years ago by Socrates (Plato, 2009), reworked in the 1950's by Benjamin Bloom (1956), and addressed in a recent study by Wagner (2010). Updated federal standards reflect this philosophy, raising the bar for teachers to educate students to think critically and emphasize creative problem solving (Standards, 2011). Research data show that students' perceptions of learning activities as being creative and meaningful are significant indicators of interest for lessons (Vekiri, 2010). With full agreement on the significance for critical and creative thinking on the part of administrators, teachers, and students, it is imperative to understand how capabilities to execute these skills may be compromised or enhanced as a result of exposure to digital lifestyles, which is the motivation behind the current study.
There is an abundance of prior research and literature that have painted a dire forecast for how exposure to modern day technology affects the capacity to learn (Bergen, Grimes, and Potter, 2005; DeStafano and LeFevre, 2007; Ophir, Nass, & Wagner, 2009; Seller, 1999). However, most of the focus of the research has been directed at testing skills that are associated with recall of facts and short term memory operations. The current study integrates an assessment of creative and higher order thinking skills as potential correlates with preferred reading text, habits to use digital technologies, and involvement in multi-tasking behaviors. The present research accomplishes these goals via quantitative research seeking to establish relationships between individuals' abilities to use creative thinking and quantitative/ literal reasoning with tendencies to multi-task and extent of exposure to traditional reading versus digital sources. One of the primary objectives of this study is to investigate potential correlates between creativity and higher order thinking skills and individuals who have divergent digital and multi-tasking habits.
The need to maximize curriculum effectiveness to support creativity and higher order thinking skills arises from a current state of affairs that is alarming to many educators. One indication of how well students enrolled in higher education are doing academically stems from opinions of college professors who work with them on a daily basis. A recent study found that 94 percent of college professors felt that their students were not prepared to write at a collegiate level (Sanoff, 2006). In 2005, only 51 percent of high school graduates who took the ACT met the college-readiness benchmark in reading, while an even more saddening percentage of only 21 percent met the benchmark in all subjects combined (math, science, English, reading) (Bauerlein, 2008). There are several aspects of this issue that will benefit from an increased awareness of the effects that digital technology has on the cognitive ability of youth. One is the extent that technology is embraced and utilized as a medium in the classroom. There is a current push to include technology in a wider array of teaching resources, but could this contribute to a dulling of cognitive potential by overexposure to the medium? Schools and culture alike have taken on the promise that technology holds for education, out of both a need to improve a system that constantly struggles to improve itself and a requisite to stay relevant with a generation that will have it no other way. However, as schools adapt to keep abreast with the fast pace of technological innovation, there is a need to make decisions concerning how technology is implemented in the classroom that is based on an awareness of how digital habits are affecting learning outcomes.
It is difficult for parents to modulate the extent to which technology plays a role in their children's lives, but knowledge about how this exposure could be affecting cognitive skills may influence what actions some guardians decide to take. This may be especially relevant for how technology is introduced into lives of children who have not yet begun traditional schooling, as this is an important time period for developing cognitive skills and not influenced as much by peer pressure. Current research indicates preschoolers are more likely to use a laptop than play outside (Moses, 2012).
Another aspect concerns the degree to which teacher and parent preconceptions of negative effects of exposure to technology have on student outcomes. The significance of teacher and parent expectations and the degree to which they are positively associated with both students' belief in their own abilities and academic outcomes is well documented (Beard, Hoy, & Hoy, 2010; de Boer, Bosker, & van der Werf, 2010; Hinnant, O'Brien, & Ghazarian, 2009; Jacobs & Harvey, 2010; Rubie-Davies, Peterson, Irving, Widdowson, & Dixon, 2010; Vekiri, 2010). Negative perceptions on the part of teachers and parents about the learning potential of youth, due to a vast increase in habitual attention to digital technology, could have a deleterious impact on student outcomes. In other words, regardless of whether claims about digital habits impairing cognitive development are true, the negative expectations that result from them could affect the potential for students to excel.
Reading trends have also been affected by the change in habits brought about with the evolution of technological innovation, both by what is read (e-books versus traditional books) and the manner in which reading is done (directed attention versus split attention). Additionally, reading is morphing into a multi-tasking activity that utilizes the capacity of the internet to follow any line of thought that may result from studying a particular subject (Wolf, 2007). The extent to which these newly evolved reading habits may be contributing to a lesser capability to focus on creative problem solving is of prime concern for all involved in the field of education.
Measuring creativity and the associative component of divergent thinking has been successful in a number of prior studies (Feldhusen, Treffinger, Van Mondfrans, & Ferris, 1971; Mumford, Baughman, Costanza, Uhlman, & Connelly, 1993; Plucker & Renzulli, 1999). However, there is some discussion concerning the correlation of creativity with the ability to generate superior performance in the context of timed measures, and whether metacognitive factors are involved without regard to speed that can be used to legitimately characterize degrees of creative ability (Wickes & Ward, 2006). Creative measures rely on the production of original thinking tasks on demand, in a testing environment that is removed from the natural environment. The fact that some individuals summon the motivation to respond creatively in this setting has been identified as one of the factors that portray creative functions (Runco, 1987; Winner, 2000).
RESEARCH SUGGESTING NEGATIVE CONSEQUENCES OF DIGITAL USE
While there is some hope for enhancing learning through a system that offers a multimedia approach, several studies reveal that the data do not meet those expectations. In a recent study, college students were tested on ability to recall facts from two different versions of a newscast. One version reported four stories while info-graphics and textual news ran across the screen simultaneously. The other newscast ran the same four stories but without the added material. Subjects that watched the pared down version did a better job of recalling specific facts and details. Bergen, Grimes, and Potter (2005) concluded that the multi-message format disrupted the attention capacity and students' ability to absorb the material, thus impacting memory retention and learning outcomes. The Bergen et al. study tested two groups of students and their ability to recall information from a lecture. One group used laptops during the lecture while the other group used pen and paper, a more traditional note taking strategy. Data indicated that students who used pen and paper scored significantly better on the assessment following the lecture. These two studies provide data that suggest the potential for both multi-tasking and digital use to impair cognition and memory retention.
In 1999, Zhu experimented with the effects of reading comprehension when using digital sources. The issue was whether the number of web links within the passage would play a significant role in distracting readers. Results indicated that the number of links correlated strongly with a condition Zhu termed "cognitive overload." In other words, the links offered too much information for the readers to assimilate concurrently with the task of comprehending and synthesizing the main ideas of the narrative.
Ophir, Nass, & Wagner (2009) found that individuals who reported high use of digital resources were more easily distracted and more likely to commit errors when asked to conduct basic short-term memory tasks related to spatial memory, rote memorization, and ability to successfully complete a simple task. Similar research focused strictly on short term memory skills and inferred comprehension differences solely from eye movement, as the patterns for internet users tended to be more scattered and haphazard than traditional readers (Nielsen & Pernice, 2009). More specifically, the eye pattern for reading digital material resembled the shape of the letter F, indicating that material on the lower right portion of the page tended not to be read. The current study contributes to the literature by expanding on these ideas, specifically investigating potential impacts on higher order thinking and providing comparative data.
In a recent meta-analysis, DeStafano and LeFevre (2007) reviewed 38 studies that involved reading with digital technology. They reported that the increased potential of enriched information offered by the internet was more than the human brain could effectively handle (also see Miall & Dobson, 2001; Sweller, 1999). DeStafano and LeFevre suggested that the evolution of the information delivery system was expanding at a pace that challenged the brain's ability to keep up. Thus, while the internet offers more information at faster intervals, the mind cannot assimilate and make sense of the data.
RESEARCH SUGGESTING POSITIVE CONSEQUENCES OF DIGITAL USE
While not as prevalent as the amount of research suggesting negative consequences for the integration of technology and education, there is evidence that the digital landscape holds promise for enhancing the capability to learn in an academic environment. When the internet was first introduced to the educational community in the 1980's there was enthusiasm from some academic circles, exemplified by the literary theorists George Landow and Paul Delany. Landow and Delany (2001) expressed the view that the internet provided a model for learning that was better related to the experience of making associations with the material, while empowering the reader to take more responsibility to challenge information, make personal connections to the ideas presented, and follow related lines of questioning.
As mentioned above, early studies testing the ability of readers to answer questions based on using either digital mediums or traditional material showed cognition problems for the digitally focused groups (Miall & Dobson, 2001; Sweller, 1999; Zhu, 1999). However, one of the confounding variables related to these studies involved the relatively new nature of computer literacy for students that lacked regular exposure to it as a learning resource and the more demanding task of assimilating a greater variety of material at a faster pace (Rouet & Levonen, 1996). It seemed while the brain had the ability to quickly adapt to the expanding rapid informational sources available through the internet, there was a lag in the ability of those newly activated systems of the brain to show gains in knowledge via traditional models of assessment. A metaanalysis that focused on comparing which educational platforms were most effective predicted that given additional time, people would become more literate with the digital medium and the cognition lag would likely diminish.
Studies by multiple researchers (e.g. Kawashima, 2005; Mori, 2002) make an important distinction between passive and active digital activities. Watching TV, movies, and casual web browsing do not involve the attention and focus required by some video games and online interactive activities such as gambling and stock trading. While some research warns that too much time spent playing video games could cause atrophy in the development of frontal lobe activity (related to communication and cognitive processes), other studies show a potential for regulated time spent at computer games to improve cognitive ability and multi-tasking skills (Kearny, 2007; Rosser et al., 2007; Small & Vorgan, 2008). Participants who played games for eight hours each week showed significant gains with an ability to successfully complete multiple tasks simultaneously (see Kearny, 2007).
James Rosser and associates (2007) discovered an inverse relationship between laparoscopic surgeons who played video games for more than three hours each week and the number of errors made during surgical procedures. Specifically, results indicated that video game playing surgeons made 40 percent fewer procedural errors, compared with surgeons who did not play video games. Small and Vorgan (2008) recognized that "gaming in moderation could help develop improved pattern recognition, more systematic thinking, and better executive skill" (p. 39); however, it is believed that the key terms within this statement are "in moderation."
Small and Vorgan (2008) also reported that the daily use of computers stimulated areas of the brain that were unaffected by reading in the traditional sense. Results showed the left side of the dorsolateral prefrontal cortex, was more highly activated in subjects that were internet-savvy compared with those that read text in the traditional fashion. Additionally, even after five days of practice for the subjects who were not heavy internet users, that part of the brain showed a significant increase in activity. Their findings suggest that the sensitive nature of the brain's plasticity and the extent to which a relatively small amount of time spent with the computer could positively affect brain function. Further research needs to address the extent to which digital involvement can facilitate brain function or how much is too much.
SUMMARY OF RESEARCH
From our current vantage point, which lies amid the evolution of these new cultural trends, it is difficult to cast a substantive verdict on the value or detriment of digital habits, hence the disparity of results when trying to come to agreement on whether digital use is supportive or disruptive to educational outcomes. To address this disparity and work toward better understanding of educational consequences, the current study integrates an assessment of creative and higher order thinking skills as potential correlates with preferred reading text and involvement in multi-tasking behaviors. In taking a quantitative approach, the research seeks to establish relationships between individuals' abilities to use creative thinking, quantitative reasoning, and literal reasoning with tendencies to multi-task and extent of exposure to traditional reading versus digital sources. The objectives are to test for potential relationships that may bring to light the advantages and/or disadvantages of digital use, specifically regarding reading material, and how digital use may impact students' and educators' abilities to successfully solve problems involving creative aptitude and higher order thinking processes. The present study hypothesized that creative and higher order thinking skills may not be impeded to the same degree that short term cognitive skills seems to be, due to living in a digital culture.
The sample was drawn from students, faculty, and staff at a private liberal arts institution in southeastern Kentucky. Participant selection for Phase I of the study was based on email correspondence to all members of the college community. Responses from this phase of study involved 151 electronic submissions, 47 males and 104 females, and all participants were at least 18 years of age. Participants in Phase I were compensated for their participation with coupons from Dairy Queen and a $5.00 gift certificate valid at the college bookstore. Participants for Phase II were recruited from the pool of participants who completed the first phase of the study. They were recruited via both email and telephone requests. Participants in Phase II were compensated with $10.00 in cash and a coupon to the local Dairy Queen, in addition to their compensation for participation in Phase I.
For reasons explained below (see discussion section), participants were grouped into two groups based on age: (1) 18 - 30 years of age, and (2) 31 years of age and older, some greater than 60 years of age ([N.sub.Males 18 - 30 years] = 27; [N.sub.Males 30 and above] = 20; [N.sub.Females 18 - 30 years] = 55; [N.sub.Females 30 and above] = 49). Demographic data, specifically age, were collected using bracketed categories (oldest category was '60 and above').
During Phase I, participants were asked to complete a survey designed by the researchers to measure the following variables: (1) reading material preferences (tendency to prefer digital versus traditional sources), (2) amount of time spent engaged in reading, for either pleasure or work, and (3) the tendency to multi-task, which was based on the likelihood to engage in multiple behaviors at the same time. The purpose was to gather data that presented a profile that documented to what extent participants used technology on a habitual basis, and to what extent those habits correlated with reading habits. This was an eight question, online, multiple-choice assessment administered via Survey Monkey[C]. Based on data collected from the online survey, participants were ranked based on total number of hours spent reading, preference for type of reading material, tendencies to multi-task, and total number of hours spent working with digital media. To maximize the differences in the habitual use of technological and reading habits between the two samples needed for the study, forty-five individuals were chosen from approximately the upper and lower thirds of the sampling population to participate in Phase II; 13 males and 32 females, ranging in age from 18 to 60 and above ([N.sub.Males 18 - 30 years] = 9; [N.sub.Males 30 and above] = 4; [N.sub.Females 18 - 30 years] = 20; [N.sub.Females 30 and above = 12).
Participants recruited for Phase II of the study were divided into two primary groups. Group 1 (N = 20), labeled multi-tasking/digital, consisted of individuals whose data in Phase I reported high reliance and use of digital resources, a strong tendency to multitask, and spent less time reading (either digital or traditional sources) for either pleasure or work. Group 2 (N = 21), labeled high read, consisted of individuals who preferred to engage in reading behavior using more traditional forms, with low reliance on digital sources, tended to multi-task to a lesser degree, and reported more time spent reading for either pleasure or work. Four individuals, included in the sample for Phase II, were classified as 'both,' such that their reported scores placed them in both categories. These individuals were digitally-oriented, high multi-taskers, and spent a lot of time reading traditional as well as digital sources for pleasure and work.
According to the Geneplore model, the process of creativity involves a generation stage (divergent thinking) and an exploration stage (convergent thinking) (Kaufman, 2012; Korba, 1993). The current study used a combination of instruments to address both components of the creative process. The Abbreviated Torrance Test for Adults (ATTA) is a well know measure that targets divergent thinking and other problem solving skills (Cho, Nijenhuis, van Vianen, Kim, & Lee, 2010). The Quantitative and Literal Reasoning Assessment (QLRA) is a behavioral measure created for the purpose of measuring convergent thinking by the authors of the present study to test critical thinking, quantitative and literal reasoning skills, and creative problem solving. The QLRA was tested during a pilot study during the fall of 2010 with a group of seven students and one faculty member. Based on results from the pilot study, decisions were made regarding the reliability and validity of the questions that were eventually used in the study.
During data collection for Phase II, participants were asked to attend a one-hour session on the college campus where they completed two assessments. The first assessment, titled the Quantitative and Literal Reasoning Assessment, contained 11 questions, and was used to measure higher order thinking. Students were allotted forty minutes for completion, followed by a five-minute break. Participants then completed the Abbreviated Torrance Test for Adults (ATTA) (Goff & Torrance, 2002; Scholastic Testing Service, Inc.), which measured creativity. This assessment consisted of three questions with three minutes allotted for each, totaling nine minutes of testing. These three questions measured individuals' abilities to think creatively in verbal and figural contexts, measuring fluency, originality, and flexibility of thinking. This assessment included a verbal section: "thinking creatively with words," and a nonverbal or figural section: "thinking creatively with pictures." Prior research indicated that the reliability coefficient for assessing both components of the ATTA was greater than 0.90 (Sweetland & Keyser, 1991). According to Treffinger (1985), test-retest reliabilities of the various sub dimensions commonly lay between 0.60 and 0.70. The ATTA consisted of four norm-referenced abilities: 1) fluency: the ability to produce quantities of ideas which are relevant to the task instruction; 2) originality: the ability to produce uncommon ideas or ideas that are totally new or unique; 3) elaboration: the ability to embellish ideas with details; and 4) flexibility: the ability to process information or objects in different ways, given the same stimulus (Abbreviated Torrance Test for Adults: Goff & Torrance, 2002).
All data were collected following strict procedures with the researcher for each session following a written script and protocol. Data collection for Phase I ran from November 5, 2010 to January 13, 2011. Data collection for Phase II began on February 21, 2011 and ended on April 16, 2011.
Primary analyses for phase II were conducted between the two sub-groups described above: (1) multi-tasking/digital and (2) high-read. Secondary analyses involved age classification that resulted in two groups, younger (18-30 years of age) and older participants (31 years of age and older).
Scoring of the ATTA followed the procedure outlined in the Abbreviated Torrance Test for Adults Manual (Goff & Torrance, 2002). Independent samples t-tests, adjusting for groups of different size, were used to calculate mean group differences of the cumulative score on the Quantitative and Literal Reasoning Assessment. Using data collected from the Quantitative and Literal Reasoning Assessment additional comparisons were made on three dependent variables: (1) quantitative reasoning: questions measuring mathematical skills and applied math, (2) creative problem solving: questions that required participants to design creative alternative solutions to proposed situations, and (3) literary reasoning: questions that required solving word association problems and recognizing relationships among variables. Inter-rater reliability for scoring of the ATTA was assessed using Pearson Product Moment Correlation Coefficient.
Independent samples t-tests, adjusting for groups of different size were also used to compare the two subgroups multi-tasking/digital and high read sub-groups on the ATTA, specifically, assessing quantitative differences across a variety of mental characteristics relevant to creativity and higher order thinking. Branching from this analysis, the individual components of this assessment: fluency, originality, elaboration, and flexibility were compared between groups. Additional analyses compared participants ranging from 18 to 30 years of age to participants 31 years of age and older, across various dimensions, including time spent reading, multi-tasking behaviors, and digital habits, as well as results from the ATTA and the Quantitative and Literal Reading Assessment.
As described above, the eight-question multiple choice survey used in phase I of the study provided data regarding digital or traditional reading material for pleasure and for academic purposes, amount of time spent engaged in reading behavior, amount of time spent in a variety of leisure activities, and amount of time spent performing activities at the same time, i.e. likelihood to engage in multi-tasking behavior. Twenty participants who reported strongest tendencies to multi-task, use digital resources to a greater extent, and read less were placed in the multi-tasking/digital group, and twenty-one participants who reported strongest tendencies to read traditional material, multitask less, and spend less time using digital resources were placed in the high read group. There were four individuals who scored high in both categories. A Pearson Product Moment Correlation was computed, revealing a significant negative correlation between the groups, suggesting that the two groups were in fact composed of different individuals who did not report similar characteristics in regard to reading and multitasking behavior. This strong, negative correlation allowed us to systematically divide our participants into two groups for additional analyses. Furthermore, this relationship indicated that individuals who read more tended to multi-task less (r(38) = -0.611, P < 0.001). Seven participants were eliminated from this statistical test: three participants failed to answer all pertinent questions necessary for calculation of the multi-tasking index and four participants' self-reported data met the criteria for both the multi-tasking/digital and high-read groups; thus, only 38 individuals were included in this statistical test. Data belonging to these participants were included in the following analyses where appropriate. Data from only one participant were excluded from analyses as this individual listed a language other than English as his native language.
There were no significant differences found when comparing the multi-tasking/digital group to the high-read group in regards to cumulative score on the Quantitative and Literal Reasoning Assessment (t(39) = -1.42,p > 0.05;[M.sub.MD] = 139; [M.sub.HiR] = 1.61; d = 0.46). There was a trend toward significance when comparing the multi-tasking/digital individuals to those classified as high readers, revealing higher mean scores in literary reasoning for the individuals in the high read group (t(39) = -1.73, p = 0.09; [M.sub.MD] = 0.46;[M.sub.HiR] = 0.58; d = 0.55; see Figure 1). The two groups did not differ from one another in regards to quantitative reasoning (t(39) = -0.70,p > 0.05; [M.sub.MD] = 0.55; [M.sub.HiR] = 0.62) or creative problem solving (t(39) = -0.44,p > 0.05; [M.sub.MD] = 0.38; [M.sub.HiR] = 0.41). Four individuals met the criteria for both groups, thus these four participants were not included in these group level analyses. Additionally, the two groups did not significantly differ from one another when comparing cumulative scores on the ATTA, although the mean for the high read group was slightly higher (t(39) = -0.64,p > 0.05; [M.sub.MD] = 68.0; [M.sub.Hir] = 71.1; d = 0.2).
For further analyses, individuals were split into two groups based on age: those aged 18 to 30 years (N = 29) and those 31 years of age and older (N = 16). Before examining the assessments, it was of interest to see if there were overall differences by age in regard to time spent reading and one's tendency to multi-task. Based on data collected from Phase I, older participants who participated in Phase II, those aged 31 years of age and older, were significantly more likely to report involvement in reading behavior and spent more time reading for either work or pleasure when compared to the younger participants, those ranging in age from 18 to 30 years of age (t(43) = -4.31,p < 0.001;[M.sub.18 - 30 years] = 3.45;[M.sub.31 and above] = 4.90; d = 1.37; see Figure 2).
Furthermore, results showed that older participants were significantly less likely to engage in multi-tasking behaviors (t(40) = 4.21, p < 0.001; [M.sub.18 - 30 years] = 2.41; [M.sub.31 and above] = 177; d = 1.41; see Figure 3). Three participants were eliminated from this statistical test due to pertinent questions necessary for calculation of the multi-tasking index being left blank or unanswered; thus, only 42 individuals were included in this statistical test. These significant differences by age led to additional analyses testing for performance differences between the two age groups. When comparing performance on the Quantitative and Literal Reasoning Assessment, although not significant, findings indicated that older participants, those aged 31 and above, tend to perform better (t(43) = -1.79, p = 0.08; [M.sub.18 - 30 years] = 1.38; [M.sub.31 and above] = 1.64; d = 1.04) on this assessment.
More specifically, older participants out performed younger participants when asked to complete questions involving quantitative reasoning (t(43) = -2.47, p = 0.018; [M.sub.18 - 30 years] = 1.00; [M.sub.31 and above] = 1.44; see Figure 4). Using these same groupings, there were no significant differences based on age on the ATTA, although older participants earned higher scores (t(43) = -0.29,p > 0.05; [M.sub.18 - 30 years] = 68.76;[M.sub.31 and above] = 70.19; d = 0.09). Thus, with a larger sample to compare differences in higher order thinking abilities according to age, data may reveal significant results.
Inter-rater reliability was assessed for the calculated scores on the Abbreviated Torrance Test for Adults. Raters followed the instructions outlined in the Abbreviated Torrance Test for Adults Manual for grading of all parts of the assessment. Both raters graded 20 completed assessments, compiling 44.4% of the total sample. Reliability was found to be very strong for all sections of this assessment (r = 0.93,p < 0.001).
As evidenced in the literature review, there is ample support to identify an inverse relationship between individuals who tend to multi-task and respective short-term memory skills (e.g. Ophir et al., 2009). The present study sought to add to the existing literature by examining potential relationships between multi-tasking, daily use of digital technology, and tendencies to read on a daily basis, with creative and higher order thinking skills. Results in the present research indicated no significant differences for the dependent variables, including scores on the ATTA, scores on the Quantitative and Literal Reasoning Assessment, as well as the individual sub-dimensions of each instrument. It was hypothesized that creative and higher order thinking skills may not suffer to the same degree that factual recall and short-term memory skills seem to do as a result of growing up in the digital age, and the data support that contention. One of the primary reasons for conducting the present study was the lack of prior research that makes a distinction between short term memory skills and higher order thinking. Given a clear distinction between the two groups tested (high read and multi-task/digital), and the variety of instrumentation used, findings indicated that the skill sets necessary for being creative and using higher order thinking may not be compromised by daily habits that reinforce multi-tasking behavior and shortened periods of focus on any single topic.
Participants in the two groups (high read and multi-task/digital) were distinguished by a difference in the use of traditional reading materials, and findings indicated that those with stronger reading did not score significantly better on the assessment. The habits formed by reading certainly have a relationship with the development of overall intellect and the exercise of complex thinking that forms the basis for creative and higher order thinking. Johnson (2006, p. 22) spoke about the mental work involved in processing and storing information when reading, networking synapses in new patterns that support use of knowledge. Small and Vorgan's (2008) research showed that use of digital technology activates areas of the brain distinct from those that are stimulated by traditional reading habits, calling into question how society's technological lifestyles may be detrimental in some ways and advantageous in other ways. Further illustrating this point is a study in 2001 that compares respondents' abilities to recall details about plot, setting, and imagery after reading literature on either traditional or digital sources (Miall & Dobson, 2001). Results from this research contended that the digital medium was a distraction, contributing to a diffusion of focus toward recalling information from the reading. A study that sought to measure respondent's abilities to extrapolate deeper meaning from the story and create new material based on participant perceptions might not have found equivalent results.
Results of the present study revealed a clear distinction between younger participants (those ranging in age from 18 to 30 years of age) and older participants (those 31 years of age and older) in terms of their tendency to use traditional reading sources and their tendency to multi-task. Data revealed that participants aged 31 years and older reported a lower frequency of technology use during daily life. Additionally, older participants, as defined for the present study, were more likely to read for pleasure and less likely to multi-task. The motivation to test for differences between these groups stemmed from the hypothesis that people who have grown up with digital technology and have strong multi-tasking habits will be less successful when faced with quantitative and literary problems to solve, based on recent prior research (Abelson, Ledeen, & Lewis, 2008; Bauerlein, 2008; Carr, 2008; Jackson, 2008; Jacoby, 2008; Keen, 2007; Postman, 1993; Shaughnessey & And, 1994; Siegel, 2008). To examine digital use in both college students and educators, division of our participants into two age groups allowed an examination of preferred reading material, digital use, and creativity in both a traditional age college student sample and an older sample, representative of most educators. The cut off age of 30 years was both an intuitive and a statistical decision. The differences in multi-tasking tendencies, time spent reading, and digital habits proved to be significant factors in the analyses of data during Phase I of the study when using 30 years of age as a defining line.
In conjunction with recent research, the present study found that younger individuals, who were significantly more likely to prefer digital media, engage in multi-tasking behaviors, and spend less time reading traditional sources, scored significantly lower on the quantitative measure for higher order thinking skills. Research by Prensky (2001) showed a generational gap between Digital Immigrants (those born and raised prior to the advent of the computer) and Digital Natives (those born and raised amidst digital technology) with respect to multi-tasking skills. To the extent that quantitative reasoning skills depend on a single focus of concentration, results in the present study may reflect a negative repercussion of exposure to digital media and the subsequent tendencies to multi-task. This study focused on quantitative skills specifically associated with higher order thinking, and it is important to note that multi-tasking may not have negative effects on using more factual based mathematical skills. A further limitation of present results indicating differences in quantitative reasoning stems from the instrument devised for the study. While pre-tested prior to the actual study, future work in this area would do well to affirm that questions used in measurement correlate strongly with higher order mathematical reasoning skills.
A limitation of the present study that will confound anyone trying to measure creativity and higher order thinking skills with quantitative instruments in a shortterm testing environment is the validity of the data, and whether results gained from instruments assess the constructs. Creativity and higher order thinking manifest in many different forms and contexts; however, this study chose to focus on those behaviors that are expected within a classroom teaching environment. The instruments used to measure those variables, as well as the way the tests were organized, do have close association with original thinking and problem solving that is expected within a classroom environment, but should not be construed to have implications for a broader concept of the terms.
It is important to note that the lack of significance found when comparing creativity scores on the ATTA indicates a need for qualification in passing judgment on the potential detriment of multi-tasking and use of digital sources to a high degree, while favoring traditional reading sources as a model for study. While short term memory skills may be at risk for individuals who exhibit high digital characteristics (Nielsen & Pernice, 2009; Ophir et al., 2009), it appears there is no significant effect on creativity and higher order thinking skills. Both sets of skills are important components for learning, but it is creativity and higher order thinking which have been viewed as having greater value as end products of the academic experience. Therefore, it is important for the academic world to also understand the potential benefits that accompany the use of digital media and to further investigate how these resources may contribute to helping educators succeed in achieving educational goals and learning outcomes.
Abelson, H., Ledeen, K., & Lewis, H. (2008). Blown to Bits: Your Life, Liberty, and Happiness After the Digital Explosion. Indiana: R.R. Donnelley.
Bauerlein, M. (2008). The dumbest generation: How the digital age stupefies young Americans and jeopardizes our future. New York: Penguin.
Beard, K., Hoy, W., & Hoy, A. (2010). Academic optimism of individual teachers: confirming a new construct. Teaching and Teacher Education: An International Journal of Research and Studies, 26(5), 1136-1144.
Bergen, L., Grimes, T., & Potter, D. (2005). How attention partitions itself during simultaneous message presentations. Human Communication Research, 31(3), 311-36.
Bloom, B. (1956). Taxonomy of educational objectives. Reading, MA: Addison Wesley Publishing Company.
de Boer, H., Bosker, R. J., & van der Werf, M. C. (2010). Sustainability of teacher expectation bias effects on long-term student performance. Journal of Educational Psychology, 102(1), 168-179.
Carr, N. (2008). The Big Switch: Rewiring the World, from Edison to Google. New York: W.W. Norton & Company.
Cho, S., Nijenhuis, J., van Vianen, A. M., Kim, H., & Lee, K. (2010). The Relationship between diverse components of intelligence and creativity. Journal of Creative Behavior, 44(2), 125-137.
DeStefano, D., & LeFevre, J. (2007). Cognitive load in hypertext reading: a review. Computers in Human Behavior, 23(3), 1616-1641.
Feldhusen, J. F., Treffinger, D. J., Van Mondfrans, A. P., & Ferris, D. R. (1971). The relationship between academic grades and divergent thinking scores derived from four different methods of testing. Journal of Experimental Education, 40, 35-40.
Goff, K., & Torrance, E. P. (2002). Abbreviated Torrance Test for Adults Manual. Bensenville, Illinois: Scholastic Testing Service.
Hinnant, J., O'Brien, M., & Ghazarian, S. R. (2009). The longitudinal relations of teacher expectations to achievement in the early school years. Journal of Educational Psychology, 101(3), 662-670.
Jackson, M. (2008). Distracted: The Erosion of Attention and the Coming Dark Age. Amherst: Prometheus Books.
Jacobs, N., & Harvey, D. (2010). The extent to which teacher attitudes and expectations predict academic achievement of final year students. Educational Studies, 36(2), 195-206.
Jacoby, S. (2008). The Age of American Unreason. New York: Vintage Books.
Johnson, S. (2006). Everything bad is good for you. New York: Penguin.
Kaufman, S. B. (2012). How Convergent and Divergent Thinking Foster Creativity. Psychology Today. Retrieved from: http://www.psychologytoday.com/blog/beautiful-minds/201202/both-convergent-and-divergent-thinking-are-necessary-creativity
Kawashima, R. (2005). Train your brain: 60 days to a better brain. Teaneck, NJ: Kumon Publishing North America.
Kearney, P. (2007). Cognitive assessment of game-based learning. British Journal of Educational Technology, 38, 529-531.
Keen, A. (2007). The Cult of the amateur: How blogs, My Space, YouTube, and the Rest of Today's User Generated Media Are Destroying Our Economy. New York: Doubleday.
Korba, R. (1993). Creativity and Consciousness in Problem Solving: Creative Cognition and the Modular Mind. Paper presented at the Annual Meeting of the Speech Communication Association (79th, Miami Beach, FL, November 18-21, 1993). Full Text from ERIC Available online: http://www.eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED368016
Landow, G., & Delany, P. (2001). Hypertext, hypermedia and literary studies: The state of the art. In R. Packer & K. Jordan (Eds.), Multimedia: From Wagner to Virtual Reality (pp. 206-218). New York: W. W. Norton.
Miall, D.S., & Dobson, T. (2001). Reading hypertext and the experience of literature. Journal of Digital Information, 2(1). Retrieved from http://journals.tdl.org/jodi/article/view/35/37
Mori, A. (2002). Terror of Game-Brain. Tokyo, Japan: NHK Books.
Moses, L. (2012). Data points: Wired child preschoolers have more exposure to electronics than ever. ADWEEK. Retrieved from http://www.adweek.com/news/technology/data-points-wired-child-143732
Mumford, M. D., Baughman, W. A., Costanza, D. P., Uhlman, C. E., & Connelly, M. S. (1993). Developing creative capacities: Implications of cognitive processing models. Roeper Review, 16, 16-21.
Nielsen, J., & Pemice, K. (2009). Eyetracking Web Usability. New Riders Press: Berkeley, CA.
Ophir, E., Nass, C. L., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the national Academy of Sciences of the United States of America, 106, 15583-15587.
Plato. (2009). The Complete Works of Plato. Akasha Classics: Iowa.
Plucker, J. A., & Renzulli, J. S. (1999). Psychometric approaches to the study of human creativity. In R. J. Sternberg (Ed.), Handbook of Creativity (pp. 35-61). Cambridge, MA: Cambridge University Press.
Postman, N. (1993) Technopoly: The Surrender of Culture to Technology. New York: First Vintage Books.
Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, MCB University Press 9(5).
Rosser, J. C., Jr., Lynch, P. J., Cuddihy, L., Gentile, D. A., Klonsky, J., & Merrell, R. (2007). The impact of video games on training surgeons in the 21st century. Archive of Surgery, 142, 181-186.
Rouet, J. F., & Levonen, J. J. (1996). Studying and learning with hypertext: empirical studies and their implications. In J. Rouet, J. J. Levonen, A. Dillon, and R. J. Spiro (Eds.), Hypertext and cognition (pp. 9-15). Mahwah, New Jersey: Lawrence Erlbaum.
Rubie-Davies, C. M., Peterson, E., Irving, E., Widdowson, D., & Dixon, R. (2010). Expectations of achievement: student, teacher and parent perceptions. Research in Education, 83(1), 36-53.
Runco, M. A. (1987). The generality of creative performance in gifted and nongifted children. Gifted Child Quarterly, 31, 121-125.
Sanoff, A. P. (2006). A perception gap over students' preparation. The chronicle of higher education. Retrieved from: Gap-Over/31426/
Shaughnessy, M., & And, O. (1994). Reading And Television: Some Concerns; Some Answers! [e-book]. Available from: Ipswich, MA: ERIC.
Siegel, L. (2008). Against the Machine: Being Human in the Age of the Electronic Mob. New York: Siegal & Grau.
Small, G. W., & Vorgan, G. (2008). iBrain: surviving the technological alteration of the modern mind. New York: Collins.
Standards (2011). Retrieved from: http://www.nap.edu/openbook.php?record_id= 4962&page=104
Sweetland, R. C., & Keyser, D. J. (Eds.). (1991). A comprehensive reference for assessment in psychology, education, and business. Austin, TX: Pro-Ed.
Sweller, J. (1999). Instructional design in technical areas. Camberwell, Australia: Australian Education Review.
Treffinger, D. J. (1985). Review of Torrance Tests of Creative Thinking. Ninth Mental Measurements Yearbook. Lincoln, Nebraska: University of Nebraska Press.
Vekiri, I. (2010). Boys' and girls' ICT beliefs: Do teachers matter? Computers & Education, 33(1), 16-23.
Wagner, T. (2010). Would you hire your own kids? 7 skills schools should be teaching them. Retrieved from: http://www.alternet.org/story/147023/would_ you_hire_your_own_kids_7_skills_schools should_be_teaching_them?page=entire
Weisberg, R. W. (1993). Creativity: Beyond the Myth of Genius. New York: W. H. Freeman.
Wickes, K., & Ward, T. B. (2006). Measuring Gifted Adolescents' Implicit Theories of Creativity. Roeper Review, 28(3), 131-139.
Winner, E. (2000). Giftedness: Current theory and research. Current Directions in Psychological Science, 9(5), 153-156.
Wolf, M. (2007). Proust and the squid. New York: Harper Collins Publishers.
Zhu, E. (1999). Hypermedia interface design: the effects of number of links and granularity of nodes. Journal of Educational Multimedia and Hypermedia, 8(3), 331-358.
Jim Rubin and Ellen H. Williams
Columbus State University, USA University of South Carolina Beaufort, USA
Correspondence concerning this article should be addressed to Jim Rubin, 4417 Conisburgh Way, Columbus, Ga. 31907 USA. E-mail: firstname.lastname@example.org
|Printer friendly Cite/link Email Feedback|
|Author:||Rubin, Jim; Williams, Ellen H.|
|Publication:||The International Journal of Creativity and Problem Solving|
|Date:||Oct 1, 2017|
|Previous Article:||Shifting the Organizational Mindset: Exploratory Evidence for the Positive Impact of Creativity Training and Strategic Planning.|
|Next Article:||Tensions in Creative Products: Using the Value Square to Model Functional Creativity.|