Flipd App Reduces Cellular Phone Distractions in the Traditional College Classroom: Implications for Enriched Discussions and Student Retention

Abstract

Cellular phone distractions inhibit faculty delivering and students learning the curriculum by reducing student attendance and active engagement. Arguably, cellular phone usage during precious class times can be a risk factor for student attrition, but scarce solutions have been offered to address this problem in colleges. The Flipd App, a cellular phone distraction reduction technology was tested on 266 college students. The results showed a positive linear relationship between usage and attendance rates and a negative linear relationship between usage and ≥ 3 absence rates, and ∼10% of students resisted its use. The Flipd App may serve as a predictive educational intervention tool that increases active learning, reduces attrition, and increases retention. It may be useful in classes with less hands-on activities to reduce distractions and increase active student engagement. The Flipd App may prove useful in helping college students curb their screen time habits from the Fear of Missing Out to the Joy of Missing Out.

Keywords

cellular phone distractions classroom distractions technology in the classroom Flipd App cellular phone Apps Fear of Missing Out Joy of Missing Out

Current U.S. college students are often distracted by their cellular phones due to the increasing demands of modern technology coupled with their need to access information instantly. The use of cellular phones in the college classroom makes it difficult for educators to obtain and sustain their student’s attention during lectures. Because information has become more accessible through cellular phones, college students are increasingly susceptible to the Fear of Missing Out (FOMO) and are less likely to engage in the world around them to experience the Joy of Missing Out (JOMO) (for review, see Aranda & Baig, 2018). These increasing student distractions through cellular phones are, in part, due to the social and peer pressures of the FOMO. Further, the peer pressure of the FOMO can subsequently have serious long-term economic consequences if the education of the next generation workforce is underdeveloped, inadequate, and/or lacking to address the problems of future generations. Further, cellular phone applications (hereon referred to as Apps) have attempted to address this very issue. In particular, the cellular phone screen lockout Flipd App offers some unique features whereby educators and students can reach a digital compromise within the college classroom (Miller, 2017). Thus, the present study investigated whether the Flipd App could influence cellular phone distractions, self-monitoring of students’ cellular phone usage in class, class attendance, and absence rates. Moreover, the study assessed whether juniors and seniors, as well as students in mathematical versus nonmathematical classes, would use the Flipd App similarly. Last, the study assessed whether the combination of an instructor’s prompts for students to stay off their cellular phones combined with the Flipd App would influence a student’s usage of the self-monitoring intervention tool. To assess these questions as the theoretical framework for the present study, the effectiveness of the Flipd App’s ability to reduce undergraduate student’s time spent on their cellular phone during lecture, as well as their class attendance and absence rates, was investigated in the context of a public 4-year primarily undergraduate institution.

Technology Advancements as Tools That Can Distract Undergraduate Students

Undergraduate students are immersed in more technological resources, better Wi-Fi connectivity since its inception in 1991 and commercialized use in 1997 (i.e., Wi-Fi is a trademarked phrase that means IEEE 802.11x), and a broader range of seemingly limitless social media platforms and venues through Apps than prior generations (for review, see Hausman, 2004; Levinson, 2004). This “technology boom” has swiftly transformed from the first zero-generation radio transmitting cellular phone invented in 1973 by Martin Cooper (Bhalla & Bhalla, 2010) that, 20 years later, morphed into a smartphone in 1993 (i.e., the Simon Personal Communicator). Since 1993, cellular phone evolution has occurred at an extraordinary pace, even for technology. For example, technology includes internet capabilities with modern fourth-generation long-term evolution data speeds (Arth et al., 2015) and today are now available in fifth-generation long-term evolution. It is theorized that at this moment in time, the risk for increased cellular phone distractions began due to the way access to information through technology became more readily accessible. Notably, with each technological advancement, an individual’s cellular phone has brought about a unique behavioral problem where people are often unaware of how much their technology distracts them from everyday living and socialization. These distractions may further impede their focus while operating a vehicle (World Health Organization & National Highway Traffic Safety Administration, 2011) and performing their responsibilities both at work and when in school.

For example, years ago, wearing a pager or beeper would only distract a person when it beeped and/or vibrated. This would then prompt the person to make a decision to call back the number and further engage now or later. In contrast, as cellular phones became more equipped with Apps, they evolved from rather small to increasingly larger digital displays that sustained people’s attention and engagement beyond the capabilities of a pager/beeper. Moreover, the evolution of cellular phones swiftly moved away from being easily concealed and stored within one’s pocket to being constantly and overtly handheld in public. Further, today’s technology has morphed into a watch/phone hybrid (i.e., Apple Watch) making the probability of one to look at their Apple Watch as frequent as they use their hands. Consistent with any desired evolutionary trait, the onset of cellular phone usage occurs at earlier ages (i.e., junior high school age) than the initial age of use (i.e., high school age) a decade ago (Divan et al., 2012) causing what has been dubbed “The Invasion of the Classroom” (Gilroy, 2004). Regarding the latter point, cellular phone distractions that often occur from junior high school through college have been argued to be a major risk factor for attrition due to increased learning history and habit of frequently experienced distractions within the classroom (Mervis, 2010). Current cellular phone technology devices and the internet have arisen from an amalgam of past analog and digital technologies such as voice, data, video, text, image, fax, graphics, personal digital assistants, and streaming media (for review, see Kleinrock, 2008). Moreover, as each cellular phone technology advancement has come to market, it too has become quickly outdated within a few years of its creation and, as a result, has created additional social pressures for having/wanting the newest technology available that is comparable to keeping up with certain styles within the fashion industry (i.e., “keeping up with the Apple Joneses”). Thus, this has created a rather short-lived commercial life cycle of technology as a constraining variable that further pressures consumers to demand newer products from the global mobile phone industry more than any other generation (Giachetti & Marchi, 2010). In the past 30 years, as cellular phones have decreased in cost, they have also increased their marketing tactics to target younger users with family plans and multiple phone devices with plan discounts, increased technology options and data storage, along with better data services and connectivity.

Therefore, it is not surprising that within most metropolitan cities, cellular phones have replaced traditional phone land lines/digital house phones (Townsend, 2000) and even at times may replace more expensive laptop computers (Rainie, 2010; Want, 2009). This constant evolution of consumer demands, to some extent, shapes and directs the creation of cellular phone manufacturing and has arguably resulted in a form of evolutionary biology through such selective pressures. This has resulted in establishing an apparent framework for “speciation of cellular phone creations” and the “destruction of such creations” (for review, see Levinthal, 1998). Arguably, the latter point can be described as one of the leading causes of electronic waste (e-waste) in the United States and globally (Kahhat et al., 2008; Robinson, 2009).

One could further argue that this cellular phone technology evolutionary phenomena to be a form of “Telephony Darwinism.” This phenomena would suggest that cellular phones pressure users to produce and receive instant access to information (i.e., which may provoke and/or condition addictive cellular phone behaviors) and perhaps increase one’s distractibility from engaging in direct and indirect socialization with others away from cellular phones (Bianchi & Phillips, 2005). It is here where the students, regardless of their educational level, are compelled by the instantaneous gratification to have information at their fingertips that can pressure even the best students to drift away from the precious lecture and miss important educational opportunities (Cho & Lee, 2016; Jacobsen & Forste, 2011). The descriptions of “rabbit holes” from “web surfing” to social media browsing, updating, checking, video and TV show streaming, FOMO, and dating Apps can significantly distract many hours of a student’s life (Thompson, 2011) through the use and abuse of cellular phones and text messaging (Tindell & Bohlander, 2011). Students are often observed in classrooms, finding themselves down a “rabbit hole” either intentionally or unintentionally due to the FOMO at the expense of their curricular learning.

Consistent with this FOMO framework, cellular phones have turned into “social media information access points and update platforms” for individuals to provide notifications to others via “life-logging” or “reality mining” (Eagle & Pentland, 2006; Gemmell et al., 2002). This has also been argued to be a new form of self-diary as “Mylifebits” (Gemmell et al., 2005), self-journalism or alternate self-journalism (Kim et al., 2009), and what has been referred to as digital storytelling (Shelby-Caffey et al., 2014). These are modern forms of digital records of autobiographical self-narratives (i.e., digital diaries) that have inherent challenges in balancing education, professionalism, entertainment, humor, journalism, and advertising for the current and upcoming generations (Deuze, 2005). This latter point has become increasingly relevant for younger second-generation immigrants as they often use digital narratives as forms of communication, self-expression, and as a means to perhaps inform close relatives and friends back home of their current status to keep in touch (Buglass et al., 2017; Hetz et al., 2015; Ranieri & Bruni, 2012).

However, less is known about digital narratives for first-generation immigrants, first time to college, and first-generation college (FGC) students as they are less defined and typically grouped together making it difficult to resolve the sensitivities experienced by select groups (for review, see Mukherji et al., 2017). Moreover, as first-generation immigrant, first time to college, and FGC populations engage in digital storytelling, they may experience and exhibit overlapping similarities with such digital autobiographical reporting and posting as second-generation immigrant populations. Notably, this digital storytelling narration is most profound in adolescent populations (Jović, 2013, 2014, 2015, 2018) from these groups establishing an early and robust set of cellular phone behaviors with high motivational traits that only strengthen as they age into adulthood. As this establishing operation of cellular phone behaviors are reinforced throughout the years within an educational environment at the junior high school and high school levels (Oberst et al., 2017), this may sensitize college-age students for acquiring more inflexible patterns of cellular phone use and abuse behaviors in the classroom (Wolniewicz et al., 2018). Thus, these cellular phone use and abuse behaviors in the classroom are contiguous with an inversely related academic learning performance history.

Further, understanding attrition has also become a major area of focus with concerted efforts aimed at identifying evidence-based risk factors for students achieving their educational goals and establishing the necessary prerequisite skills for early career development. As such, assumptions regarding persistence (i.e., otherwise often referred to as “grit”) and what they actually measure have been evaluated to better conceptualize its contribution to college student success (Park et al., 2008). In addition, promoting cellular phone usage in the classroom (e.g., Kahoot) has been suggested to circumvent the issue of distraction, but the verdict is unclear as there is no one to surveillance all students (i.e., either participating or being distracted) in every class to accurately address this issue. Arguably, cellular phone distraction within the classroom (i.e., emerging from junior high school and persisting through college) is a progressive educational problem related, in part, with the responsible/irresponsible use of technology by students. This may very well be an underexplored potential risk factor for attrition that may operate in parallel to and/or with persistence/grit.

Thus, studies aimed at clarifying persistence/grit and examining cellular phone distractions warrant further investigation. Moreover, studies that also seek to examine how alternative cellular phone technology can be used to limit such distractions are often associated with social pressures surrounding the FOMO and its motivational, behavioral, and emotional correlates (Przybylski et al., 2013). These social pressures surrounding the FOMO are desirable for educators in trying to reach as many students as possible through the lecture. In addition, these cellular phone distractions if left unaddressed may not only reduce educational learning outcomes and increase attrition but also contribute to social media fatigue and negative health consequences of one’s psychological well-being (Dhir et al., 2018). Ultimately, this may also further influence one’s academic motivation related to attrition (Alt, 2015). The latter point suggests that research is required to elucidate the relationships between social media fatigue and negative health consequences of one’s psychological well-being as a potential risk factor for college student retention and/attrition.

Thus, students may pay more attention to what is on their cellular phone than what is being taught within the classroom (Tindell & Bohlander, 2011). The strengthening of counterproductive educational behaviors (i.e., cellular phone distractions) is arguably a reinforcing risk factor for attrition at the college level. Furthermore, studying college student’s cellular phone behaviors may offer insight toward practical and effective educational classroom interventions that may increase college student retention rather than attrition. Early attrition has been examined at the doctoral level (Rudd et al., 2018) in the Educational (Pauley et al., 1999) and Science, Technology, Engineering, and Mathematic fields (Lott et al., 2009; Maher et al., 2017), Science, Technology, Engineering, and Mathematic first-year college majors (Daempfle, 2003), nursing programs (Wells, 2007), women as business major in urban Community Colleges (Karlen, 2003), and Veteran needs (Alschuler & Yarab, 2018). Further, early attrition has also been used to assess sociocultural influences in relation to academic support services (Weuffen et al., 2018), student perceptions of college (Campbell & Mislevy, 2013), including student-centered retention programs (Sieveking & Perfetto, 2001), presemester college orientation programs (Perrine & Spain, 2008), and freshman first-year programs (Colton et al., 1999; Daempfle, 2003; Kamer & Ishitani, 2019; Whiteley, 2002). Last, early attrition has also been examined in relation to whether students are FGC or continuing-generation students (Radunzel, 2018), through student demographic and attitudinal data set modeling (Glynn et al., 2005), and at the undergraduate level through data analytics from an institutional perspective (Delen, 2011).

Currently, there are increasing trends for institutions to compile a sophisticated series of data sets that can be used as predictive analytics for determining the ongoing nature of risk factors for college student attrition (for review, see Barbera et al., 2017). Despite all of these well-intended approaches to understanding student attrition, one of the best predictors of college student attrition remains as attendance. Further, along with attendance, developing more efficient ways for faculty/instructors to monitor and provide students with feedback through early warnings may help to improve student retention (Bhalla et al., 2013; Chawhan et al., 2013; Ganesh, 2016; Hudson Sr., 2005; Richie & Hargrove, 2005). However, even if students are physically present within the college classroom, they may be mentally absent from the lecture due to the social pressures associated with cellular phone technology and social media outlets as distractors.

In addition, cellular phone technologies have further evolved into digital data and Ground Position Satellite individual human mobility and interest tracking pattern generators (González et al., 2008), with intent to market and connect consumers with products and merchandise that may match their interests. This is a very important point as prior computer operations would require the user to independently search for and seek out what they were interested in (i.e., active web/social media searching), and today’s computer algorithms are actively reaching out to users prompting them to review information that they send them (i.e., passive web/social media searching). These algorithms and marketing techniques that further solicit both active and passive forms of consumerism often distract many individuals from many daily activities. This new speciation of cellular phone technologies have been socially contextualized as the “Machines that Become Us” in today’s domestication and mobile telephony (Haddon, 2002). This latter point adds yet another level of cellular phone distraction that can further impede learning within the college classroom as these algorithms continually attempt to demand the attention of their social media App platform user (e.g., pay to remove advertisements), thus further distracting them within the college classroom.

The Flipd App and Reducing Distractions Within the College Classroom: Challenges With Sustaining Student Attention With Competing Technology

In response to such cellular phone technology distractions and its associated risks for attrition, specifically in the context of the undergraduate college classroom and in the employment sector, the Flipd App was invented by Flipd, Inc. (Toronto, Canada). The Flipd App serves as a cellular phone self-awareness device that can be used to preprogram days and times to reduce and perhaps eliminate distractions that the user may typically experience (i.e., actively or passively) while in the classroom or at work. College faculty are continually facing increased pressures to improve student learning, reduce attrition, increase retention, and increase 4-year graduation rates (Austin, 2002; Austin & McDaniels, 2006). Conversely, students are equally pressured to earn good grades to achieve optimal opportunities for pathways toward career success beyond the undergraduate experience (Kuh et al., 2008; Ross et al., 1999) while staying connected to the world through their cellular phone and its range of Apps. Furthermore, student’s perceptions of how likely they will succeed in college can be greatly influenced by the campus culture and its perception of diversity (Ancis et al., 2011). In addition, at most public undergraduate universities, the increased staffing ratios of non-tenure-track faculty have been shown to adversely affect student 4-year graduation rates (Ehrenberg & Zhang, 2005; Kezar & Sam, 2010; Youn & Price, 2009).

Between these two pressurized sides of the college classroom, there is a convergent problem where undergraduate students are increasingly distracted by their cellular phones and less engaged in learning within the classroom (Berry & Westfall, 2015; Burns & Lohenry, 2010; Campbell, 2005; Fox et al., 2009; McCoy, 2016). This situation creates a theoretical model for attrition through the following risk factor sequence: (a) increased cellular phone distractions, (b) decreased learning, (c) variable or reduced classroom attendance, and (d) increased risk for attrition. In particular, the current generation of undergraduate students are more “plugged in” to their cellular phone technology than prior generations due to the FOMO (Buglass et al., 2017; Hetz et al., 2015). This poses the following educational challenges for both faculty and students within the college classroom: (a) Both faculty and student motivation could decrease as a function of less interactive or engaging lectures/discussions, (b) student preparedness (i.e., reading before a lecture and being ready to discuss material consistent with “Just-in-Time Teaching Methods”; for a review, see Novak, 2011) may be reduced substantially, thereby diminishing the scope of the curriculum to be completed within a semesters time and reduce the quality of classroom discussions, (c) faculty may be unable to catch many students up on basic principles of a given course, thereby preventing them from moving to a desired level of the curriculum, (d) these course setbacks can contribute to the widening of the academic achievement gap (Mukherji et al., 2017), and (e) the risk or fear of student failure may further demotivate students from attending classes.

Beyond these educational issues is the very real emerging problem that the current generation and those that follow are becoming more addicted to their cellular phones and, as a result, engage less in both active and passive learning while in college. Arguably, this situation is a predictive educational risk factor that may be contributing to the underpreparedness of college graduates when entering the global workforce. This is a rather serious and growing problem as more students are going to college than ever before, and the academic achievement gap is not narrowing, as the educational field would have hoped it would. Thus, faculty and college students are faced with this rather complex dual dilemma, and perhaps reducing cellular phone distractions within the college classroom may be both a logical and timely educational intervention that might mitigate these academic achievement gap and workforce preparedness issues. If these problems are not addressed appropriately, this situation could result in increased attrition, a widening of the academic achievement gap, and even for those students that graduate college, they may not provide adequate occupational services to their local communities, which, in turn, could cause issues regarding economic instability, improper health care, and a plethora of concerning societal issues.

Thus, careful consideration of the aforementioned factors is critical to reestablish sincere educational interest in the course material for undergraduate college students, increase college classroom attendance, increase active engagement in lectures with faculty and student peers as well as responsibly preparing students for subsequent lectures as a means to reduce attrition. The current study sought to examine the aforementioned questions regarding the aforementioned theoretical framework regarding undergraduate student cellular phone distractions within the classroom to assess the effectiveness of the Flipd App as a self-monitoring educational intervention tool.

Method

Participants

The study was approved by The State University of New York College at Old Westbury (SUNY-OW’s) institutional review board under an educational exemption for research purposes, as there were no risks to participants. The participants (N = 266) in this study consisted of undergraduate SUNY-OW college students from lower-level juniors (3,000), upper-level juniors (4,000), and seniors (5,000) who randomly enrolled across 14 courses within the psychology department over a 3-year period (i.e., Fall 2016 to Summer 2018). The 14 classes were comprised of 7 different courses with their name and the number of times they were examined in the parentheses that follow: PY3610 Brain & Behavior (3), PY3620 Drugs & Behavior (2), PY3120 Research Methods II: Statistics (2), PY4100 Research Methods III: Experimental Psychology (3), PY4401 Developmental Neuropathology (2), PY4402 Neuropsychopharmacology (1), and PY5404 Environmental Neuroplasticity (1). Table 1 illustrates the sample size distributions across each of the 14 classes examined in the present study. Of the 266 participants, unfortunately 32 did not use Flipd App at all across the study (i.e., 12.03% of sample population), leaving 234 participants (i.e., 87.97%) to be assessed for any relationships with the Flipd App.

Table 1.
Sample Size Distributions Across Each of the 14 Classes Examined.

Course
Class by semester (% of total sample N = 266)

Fall 2016
Spring 2017
Fall 2017
Spring 2018
Summer 2018

n % n % n % n % n %

PY3610 Brain & Behavior 28 10.53 22 8.27 N/A N/A 34 12.78 N/A N/A

PY3620 Drugs & Behavior 20 7.52 N/A N/A 32 12.03 N/A N/A N/A N/A

PY3120 Research Methods II: Statistics 17 6.39 N/A N/A 16 6.01 N/A N/A N/A N/A

PY4100 Research Methods III: Experimental Psychology 18 6.77 18 6.77 N/A N/A 17 6.39 N/A N/A

PY4401 Developmental Neuropathology N/A N/A 8 3.01 N/A N/A 10 3.76 N/A N/A

PY4402 Neuropsychopharmacology N/A N/A N/A N/A 20 7.52 N/A N/A N/A N/A

PY5404 Environmental Neuroplasticity N/A N/A N/A N/A N/A N/A N/A N/A 6 2.25

Note. N/A indicates that the course listed was not taught with the Flipd App during that semester.

Materials

The participants were asked to download the Flipd APP (Ontario, Canada) from the Google Play Store, Apple App Store, or directly from the vendor’s website at www.flipdapp.co. Each participant had an individual user license linked to the specific course that they were enrolled that could only be accessed by the participant and the researcher. The user licenses were purchased for the participants to eliminate any confounds due to individual cost affordability or other potential financial constraints. The class session days and times for each of the 14 classes during the Fall, Spring, and Summer semesters were programmed accordingly. The programmed class sessions lasted 90 minutes, and students were offered reminders of when they should “Flip Off” their cellular phones for an upcoming class. In addition, the participants were able to access their weekly cellular phone distraction statistics, set personal daily reminders, and could access feedback on how much time they spent off their cellular phones while in the college classroom as additional proactive and reactive notification prompts.

Procedures and Design

At the beginning of each class, the researcher informed all students that the Flipd App would be used to track their attendance and decrease cellular phone distractions within the classroom and that their time “Flipd Off” would be used for 5% of their total grade and the their in-classroom attendance would be used for another 5% of their total grade. Thus, a 10% grade incentive was used to motivate students to self-monitor and actively try to reduce their cellular phone usage and distractions within the 90-minutes class sessions that took place for each 16-week semester. To be clear, this 10% grade incentive was consistent with other classes that did not use the Flipd App, and extra credit was built into the classes to compensate for the 1% to 2.5% of the overall class grades to ensure that this study did not inadvertently compromise student’s final grades in the course and overall grade point average. All class sessions in which an exam, holiday, recess, inclement weather class cancellation, and so forth was scheduled/occurred were excluded from the Flipd App analyses.

Statistical Analyses

Histograms of Class Distributions and Flipd App Usage

At the end of each semester, a histogram for the seven different courses were examined (i.e., repetitions of the same course were combined to illustrate a single histogram representing that course) to evaluate the percent of the class distribution that was sampled. The aim was to characterize the participant’s use of the Flipd App through the following intervals: 0%, 1% to 10%, 11% to 20%, 21% to 30%, 31% to 40%, 41% to 50%, 51% to 60%, 61% to 70%, 71% to 80%, 81% to 90%, and 91% to 100% usage, respectively. Another set of histograms were constructed to evaluate the differences between the lower junior- (3,000), upper junior- (4,000), and senior-level (5,000) classes and for all 14 classes combined. Further, a last set of histograms were constructed to evaluate the differences between classes that required mathematics (i.e., PY3120 Research Methods II: Statistics & PY4100 Research Methods III: Experimental Psychology) and compared them against the other classes with no mathematical requirements.

Percent Difference in Flipd App Usage as a Function of Choice, Course Level, and Mathematic Requirements

In addition, the percent of the overall distribution that was sampled was examined to compare the total number of participants who chose to use versus participants who chose not to use the Flipd App. A more sensitive measurement of the Flipd App usage evaluated the total number of participants with the following criteria: participants who chose not to use the Flipd App (0%), participants who inconsistently used the Flipd App (≤ 60%), and participants who consistently used the Flipd App (≥ 61%), for the 3,000, 4,000, 5,000, and all level courses, respectively. Further, this same categorical measure was used to evaluate the difference in the Flipd App usage between mathematical and nonmathematical required courses.

Attendance Rates and Flipd App Usage Correlations

The Average Attendance Total (AT) rates were measured as the percent of classes attended divided by the total number of classes and multiplied by 100 to obtain a percent of attendance per class. In addition, within each class, the percent of participants who missed more than three classes throughout the semester were calculated (≥ 3 absences), as well as the percent of participants who did not miss a single class throughout the semester (no absences). Moreover, a range was determined for each class attendance to evaluate whether variability of course attendance would have interfered with the interpretation of the effects of the Flipd App usage within the college classroom in reducing cellular phone distractions. Further, two correlations were computed using a Pearson’s correlation (r) and its coefficient (r²) to establish an effect size of the relationship, which is mathematically equivalent to eta squared (η²). The first correlation sought to determine the relationship between the percent attendance rates and the overall percent Flipd App usage, whereas the second correlation sought to determine the relationship between the percent ≥ 3 absence rates and the overall percent Flipd App usage, respectively. A one-tailed test with a significance criteria of α = .05 and a confidence interval of 95% was carried out for the Pearson’s correlation (r) statistic.

Experimental Assessment of the Flipd App Usage in the College Classroom

To assess whether the Flipd App has value as an educational intervention tool within the college classroom, an experimental procedure with a four-phase design was introduced across three classes within the same semester. The three classes consisted of a control class and two experimental classes with four phases. The four phases were broken down into an initial baseline (Phase I), followed by an intervention (Phase II) that monitored the effects of the Flipd App “reminder prompt,” then another “reminder prompt” following midterms was provided as a probe (Phase III), and finally another assessment period to monitor the effects of the Flipd App “reminder prompt” until the conclusion of the semester (Phase IV). The control class was a PY4100 Research Methods III: Experimental Psychology class that did not have a midterm exam, the Experimental A class was a PY4401 Developmental Neuropathology class, and the Experimental B class was a PY3610 Brain & Behavior class that had a midterm exam. Phase I was the first day of classes in which all participants did not use the Flipd App, and all three classes were prompted to do so moving forward. Phase II was the participant’s natural tendency to use the Flipd App. Phase III was the participant’s Flipd App usage following a midterm in which the Experimental A class was prompted to continue to use the Flipd App, whereas the control class and the Experimental B class were not prompted. Phase IV was the resulting participant’s natural tendency to use the Flipd App across the remaining class weeks.

Results

Flipd App Usage Sample Distribution Characteristics

The sample distribution (N = 266) was comprised of 7 different courses across 14 classes, which were taught at the same days and times for the Fall, Spring, and Summer semesters, respectively. The data revealed that despite the broad range of upper division psychology courses examined, each of these classes ranged from 2.26% to 12.78% proportion (Table 1) of the total distribution with M = 7.52% (SD = 3.09; SEM ± 0.83). Thus, each class was within range of approximating a normal distribution as shown in Figure 1A.

Figure 1.
Histograms of the Sample Distribution That Used the Flipd App Parsed by Class. The classes consisted of All Courses (N =266) (A), PY3610 Brain & Behavior (n = 84) (B), PY3620 Drugs & Behavior (n = 52) (C), PY3120 Research Methods II: Statistics (n = 33) (D), PY4100 Research Methods III: Experimental Psychology (n = 53) (E), PY4401 Developmental Neuropathology (n = 18) (F), PY4402 Neuropsychopharmacology (n = 20) (G), and PY5404 Environmental Neuroplasticity (n = 6) (H) psychology courses. The left panels (B to D) represent the lower-level junior courses, whereas the right panels (E to H) represent the upper junior- and senior-level courses). The vertical dashed line delineates the above chance factor (≤ 60%) that participants consistently use the Flipd App (≥ 61%). The data reveal that as a function of increasing difficulty in the psychology course (from lower junior- to upper junior- and senior-level courses) that the distributions change from polymodal, to bimodal, and unimodal negatively skewed distributions.

Further, the attendance rates for each class were examined to ensure that participants who attended class could be evaluated on their Flipd App usage. The data revealed that each of the class attendance rates ranged from 41.38% to 100% (Table 2), with M = 89.93% (SD = 8.65; SEM ± 0.56). Moreover, the data showed that for participants who missed ≥ 3 classes ranged from 0% to 73.68% with M = 51.66% (SD = 20.66; SEM ± 5.52; Table 2). Last, the data showed that for participants who never missed a class ranged from 3.23% to 83.33% with M = 18.50 (SD = 20.50; SEM ± 5.48; Table 2). Altogether, the data suggest that the sample size yielded a sample distribution that was representative of a typical college student population of SUNY-OW and comparable to a 4-year primarily undergraduate institution. Moreover, this sample was representative of a largely diverse student population (i.e., ∼60% to 70% diverse student population) with a range of learning preferences (for review, see Mukherji et al., 2017; Neuwirth et al., 2018a, 2018b, 2019).

Table 2.
Absentee Rate for Each of the 14 Classes Sampled in the Distribution.

Course and semester
Classroom characteristics

AT % ≥ 3 % N/A % R %

PY3610 Brain & Behavior

Fall 2016 86.47 82.35 5.88 70–100

Spring 2017 89.21 67.74 3.23 75.86–100

Fall 2017 N/A N/A N/A N/A

Spring 2018 88.85 56.67 36.67 41.38–100

Summer 2018 N/A N/A N/A N/A

PY3620 Drugs & Behavior

Fall 2016 89.33 70.00 10.00 76.67–100

Spring 2017 N/A N/A N/A N/A

Fall 2017 93.10 46.67 36.67 78.57–100

Spring 2018 N/A N/A N/A N/A

Summer 2018 N/A N/A N/A N/A

PY3120 Research Methods II: Statistics

Fall 2016 90.00 64.71 11.77 76.67–100

Spring 2017 N/A N/A N/A N/A

Fall 2017 91.77 50.00 22.00 66.67–100

Spring 2018 N/A N/A N/A N/A

Summer 2018 N/A N/A N/A N/A

PY4100 Research Methods III: Experimental Psychology

Fall 2016 86.49 73.68 10.53 66.67–100

Spring 2017 91.03 57.69 15.39 70.37–100

Fall 2017 N/A N/A N/A N/A

Spring 2018 92.04 35.00 15.00 70.37–100

Summer 2018 N/A N/A N/A N/A

PY4401 Developmental Neuropathology

Fall 2016 N/A N/A N/A N/A

Spring 2017 86.42 75.00 18.75 65.52–100

Fall 2017 N/A N/A N/A N/A

Spring 2018 91.17 60.00 10.00 82.14–100

Summer 2018 N/A N/A N/A N/A

PY4402 Neuropsychopharmacology

Fall 2016 N/A N/A N/A N/A

Spring 2017 N/A N/A N/A N/A

Fall 2017 84.80 68.42 21.05 48.15–100

Spring 2018 N/A N/A N/A N/A

Summer 2018 N/A N/A N/A N/A

PY5404 Environmental Neuroplasticity

Fall 2016 N/A N/A N/A N/A

Spring 2017 N/A N/A N/A N/A

Fall 2017 N/A N/A N/A N/A

Spring 2018 N/A N/A N/A N/A

Summer 2018 99.17 0.00 83.33 95–100

Note. N/A indicates that the course listed was not taught with Flipd during that semester; AT indicates the average percent of classes all students attended; ≥ 3 indicates the percent of classes that students missed three or more classes; NA indicates the percent of students that did not miss a single class; R indicates the range of classes attended.

Flipd App Usage Sample Distribution Histograms

The data from each class were organized into histograms for each of the seven different courses where the x-axis illustrated how many participants did not use the Flipd App (0%), how many participants used the Flipd App inconsistently (≤ 60%; which was used as a chance factor lower cut off criteria), and how many participants used the Flipd App consistently (≥ 60%). The lower junior-level classes (Figure 1 B to D) showed a polymodal distribution that became more bimodal and unimodal as the courses moved toward upper junior (Figure 1 E to G) and senior levels (Figure 1H). As the courses reached the senior level, a negatively skewed distribution was observed with participants consistently using the Flipd App ≥ 61% of the course (Figure 1H). To examine the nature of this senior-level observation, the lower junior-, upper junior-, and senior-level courses were combined to create three distinct distributions to increase the sensitivity in observing this phenomenon (Figure 2). The results of the histograms for the lower junior (Figure 2A), upper junior (Figure 2B), and senior levels (Figure 2C) corroborated with the histograms from (Figure 1A to H) showing that participants consistently used the Flipd App ≥ 61% of the course more in senior-level than junior-level courses.

Figure 2.
Histograms of the sample distribution that used the Flipd app parsed by lower junior- (3,000; n = 169) (A), upper junior- (4,000; n = 91) (B), and senior (5,000; n = 6) (C)-level psychology courses. The vertical dashed line delineates the above chance factor (≤ 60%) that participants consistently use the Flipd App (≥ 61%). The data reveal that as a function of increasing difficulty in the psychology major, a larger proportion of seniors (negatively skewed distribution) (C) use the Flipd App ≥ 61% more than lower- (A) and upper (B)-level juniors (bimodal distributions), but there is an ∼7% increase in the proportion of students who do not use the Flipd App (0%) during their senior than junior years within the psychology major.

Interestingly, when the histograms were parsed by mathematical and nonmathematical required courses within the psychology major (Figure 3), mathematical courses revealed a positively skewed distribution with a larger proportion of participants who used the Flipd App inconsistently (Figure 3A), which was predicted. Remarkably, the nonmathematical required courses (Figure 3B), which would be predictive of more cellular phone distraction behaviors, actually showed a bimodal distribution with a larger proportion of the sample that consistently used the Flipd App ≥ 61% than the mathematical required courses (Figure 3A). This suggests that the Flipd App may be an effective educational intervention tool in reducing cellular phone distraction behaviors in nonmathematical college classes.

Figure 3.
Histograms of the sample distribution that used the Flipd app parsed by mathematical (n = 86) (A) and nonmathematical (n = 180) (B) required psychology courses. The vertical dashed line delineates the above chance factor (≤ 60%) that participants consistently use the Flipd App (≥ 61%). The data reveal that as a function of courses that required more hands-on mathematic skills, participants generally stay off their cellular phones around chance levels (≤ 60%) (A) with a positively skewed distribution. However, in courses that would require less hands-on activities and more freedom to use one’s cellular phone, the Flipd App increases the proportion of ≥ 61% time away from cellular phone distractions (B) with a more bimodal distribution.

Elective Flipd App Usage to Encourage Reducing Cellular Phone Distraction Behaviors in the College Classroom

Despite the incentive that class attendance comprised 5% and the Flipd App usage comprised another 5% of the participants overall course grade (i.e., which is within reason for a 10% attendance grade element in most college courses nationally), there were a proportion of participants who still chose not to use the Flipd App even if it were free. The data showed that 12.03% of participants (n = 34) chose not to use the Flipd App, whereas 87.97% (n = 234) chose to use the Flipd App, regardless if they did so consistently. When participants were asked why they did not use the Flipd App, they anecdotally reported: “they forgot,” “their phone was not compatible” (e.g., Windows or Pay as you Go plans), and “they refused to delete photos or videos of off their phone to make room for the download” (i.e., which requires only 9.6 MB of data storage). Some negative anecdotes were that because students were not clear of how the Flipd App worked, some “felt that it was an invasion of their phone privacy,” others “felt that it was restricting their choice to use their phone rather than listen or engage in a college classroom,” and others simply “didn’t care for it.” In contrast, some positive anecdotes were “Flipd help them to stay focused in class,” “it made them realize how much they were on their phone,” and “they began to make better phone choices and took a timeout from social media.” In addition, some students even reported using the Flipd App across other classes that were not included within the present study. This anecdotally reported generalization effect was a powerful observation in that participants increased their cellular phone self-awareness behaviors, the value of their education, and began to reduce their cellular phone distractions within the college classroom to increase better “screen-time” habits and course learning outcomes.

Effectiveness of the Flipd App Interventions for Juniors and Seniors in the College Classroom

To evaluate the effectiveness of the Flipd App intervention within the classroom, the classes were parsed by 3,000, 4,000, and 5,000 and averaged together as All Classes to determine the proportion of participants who did not use the Flipd App (0%), participants who used the Flipd App inconsistently (≤ 60%), and participants who consistently used the Flipd App (≥ 61%; Figure 4). The data revealed that 12.03% (range 10.71%–16.67% participants per class) of participants did not use the Flipd App regardless of course level. This indicated that approximately a little more than 12% of the sample distribution would not attempt to try to reduce their cellular phone distractions while in the college classroom. In contrast, lower junior (56.97%) and upper juniors (59.92%) inconsistently used the Flipd App more than seniors (16.67%; Figure 4). The data suggest that there is more variability in juniors (i.e., nearly two third of the sample population of juniors) than seniors to reduce their cellular phone distraction behaviors within the college classroom. Notably, seniors used the Flipd App more consistently (66.67%, which is a 50% usage increase), when compared with lower- (32.12%) and upper-level (29.76%) juniors, respectively (Figure 4).

Figure 4.
The Percent of the Sample Distribution That Did Not Use (0%), Inconsistently Used (≤ 60%), or Consistently Used the Flipd App (≥ 61%) as a Function of Lower Junior (3,000; n = 169), Upper Junior (4,000; n = 91), Senior (5,000; n = 6), and All Level Courses. The data are presented as the total per group graphed as a percentage of the total number of participants (N = 266). The data show that approximately 12.03% of participants did not use the Flipd App (range 10.71% to 16.67%) regardless of course level. In contrast, lower junior (56.97%) and upper juniors (59.52%) inconsistently used the Flipd App more than seniors (16.67%). Notably, seniors used the Flipd App more consistently (66.67%, which is a 50% usage increase), when compared with lower- (32.12%) and upper (29.76%)-level juniors, which is a usage decrease of 24.85% and 29.76%, respectively. Across all courses, the data revealed that 54.51% of participants used the Flipd App inconsistently, 30.83% used the Flipd App consistently, and 12.03% did not use the Flipd App at all.

Thus, seniors may be more responsible and focused on completing their education and paying more attention to the lectures than their competing cellular phone distractions during college classes. Across all courses, the data revealed that 30.83% of participants used the Flipd App consistently, 54.51% of participants used the Flipd App inconsistently, and 12.03% of participants did not use the Flipd App at all (Figure 4). Altogether, this suggested that approximately 10% of the sample distribution would not engage in the Flipd App, 30% of the sample distribution would use and benefit from the Flipd App, and nearly 60% of the sample distribution may try the Flipd App but use it inconsistently. At present, it remains to be elucidated what factors may help to effectively transition students from inconsistent to consistent Flipd App usage. Further, the factors that would contribute to inconsistent and consistent Flipd App usage (i.e., additional prompting, training, self-time management, self-organization, self-awareness, etc.) warrant further investigation. Further, whether the Flipd App would show similar usage for freshmen and sophomores compared with what was observed herein for juniors remains to be elucidated.

Effectiveness of the Flipd App Intervention for Mathematical and Nonmathematical Required College Courses

The data show that approximately the number of participants who did not use the Flipd App in nonmathematical (13.11%) and mathematical (9.64%) required courses (i.e., a 3.47% difference) were slightly worse in nonmathematical courses (Figure 5). Notably, 49.45% of participants inconsistently used the Flipd App in nonmathematical courses, whereas 69.88% inconsistently used the Flipd App in mathematical courses (i.e., a 20.43% decrease in Flipd App usage; Figure 5). In contrast, 35.79% of the participants used the Flipd App more consistently in nonmathematical courses, whereas 24.10% did so in mathematical courses (i.e., an 11.69% increase in Flipd App usage; Figure 5).

Figure 5.
The Percent of the Sample Distribution (N = 266) That Did Not Use (0%), Inconsistently Used (≤ 60%), or Consistently Used the Flipd App (≥ 61%) as a Function of Students Enrolled in Mathematic (n = 86) or Nonmathematic (n = 180) Psychology Required Courses. The data show that 9.64% participants in mathematical and 13.11% participants in nonmathematical classes did not use the Flipd App (3.47% difference). In the nonmathematical courses, participants inconsistently showed lower usage of the Flipd App < 60% (20.43% reduction), when compared with mathematical courses. In contrast, participants who used the Flip App > 60% used the Flipd App more consistently in nonmathematical courses (11.69% increase), when compared with mathematical courses. The data suggest that in classes that required less or no hands-on activity as in a mathematical required course, that the Flipd App may be useful in reducing cellular phone distractions while in the college classroom.

The data suggest that in classes that required less or no hands-on activity as in a nonmathematical required course, students may be at increased risk for more cellular phone distractions (i.e., as shown in Figure 5, the < 60% inconsistent and > 60% consistent Flipd usage). Further, the Flipd App may be a useful educational intervention tool that can reduce cellular phone distractions than can be differentiated by the specific types of course offerings while in the college classroom.

Relationships Between the Flipd App Intervention on College Student Attendance and Absence Rates

Despite the type of hands-on or hands-off type of class, student attendance is a critical aspect of their ability to engage with faculty through the course material they are learning. Further, absenteeism has been shown to be a clear risk factor for course attrition, whether it be due to contracted syllabi parameters or lack of being exposed to and comprehending the course material. To address this issue, a Pearson’s correlation (r) and its coefficient (r²) to establish effect size were computed. Of the 266 participants, unfortunately, 32 did not use Flipd App at all across the study (i.e., 12.03% of sample population), leaving 234 participants to be assessed for any relationships with the Flipd App. The first correlation revealed a significant moderate positive linear relationship with an r = .360 (p < .001***) between the percent attendance rates and the overall percent Flipd App usage, with an r² = .600 effect size (i.e., the Flipd App usage can explain 60% the variability in participant attendance rates in class; Figure 6).

Figure 6.
The Correlation Between the Participant’s Percent Attendance Rates (Right Histogram) and Percent Time “Flipd Off” of Their Cellular Phones (Upper Histogram). The sample distribution is comprised of n = 234 participants (scatter plot circles) across all classes assessed with the regression line (solid) and the residual lines (dashed). The data revealed a significant moderate positive linear relationship with an r = .360 (p < .001) with an effect size of r² = .600. Thus, as the Flipd App usage in the classroom increased, attendance rates also increased.

In contrast, the second correlation revealed a significant moderate negative linear relationship with an r = –.340 (p < .001) between the percent ≥ 3 absence rates and the overall percent Flipd App usage, with an r² = .587 effect size (i.e., the Flipd App usage can explain 58.7% of the variability in participant absence rates; Figure 7). Because the statistics that were used were limited to correlations rather than causation, one must approach the interpretations carefully when evaluating the Flipd App usage and college student attendance and attrition rates.

Figure 7.
Correlation Between the Participant’s Percent Absence Rates (Right Histogram) and Percent Time “Flipd Off” of Their Cellular Phones (Upper Histogram). The sample distribution is comprised of n = 234 participants (scatter plot circles) across all classes assessed with the regression line (solid) and the residual lines (dashed). The data revealed a significant moderate negative linear relationship with an r = –.344 (p < .001***) with an effect size of r² = .587. Thus, as ≥ 3 absence rates increases, the Flipd App usage in the classroom decreases.

Thus, this initial study proves promising to build upon for future studies to further assess more direct causal risk factors on course attrition with the Flipd App. Notably, the Flipd App has shown to be useful to separate out distributions of college students that may be fully distracted by their cellular phones in class (∼15%), may be inconsistently distracted by their cellular phones in class (∼55%), and may not be distracted by their cellular phones in class (∼30%). These data provide a good starting point for future studies to experimentally test whether the Flipd App can be an effective educational intervention tool (i.e., reducing cellular phone usage and increasing attendance rates) that may be a fair predictor of student classroom attendance. Whether the Flipd App may predict for students at risk of failing a course based on their absence rates remains to be investigated. Further, if the Flipd App can be used in the future to show such causal relationships, then it may be consistent with the reactive academic predictive analytic tools currently being used nationally, and perhaps it may be a more sensitive proactive educational intervention tool within the classroom to reduce college student attrition (Campbell et al., 2007).

Effect of Faculty Reminder Prompting on Flipd App Usage Across Courses

To assess the independence of college student’s ability to self-monitor and sustain their Flipd App usage, three classes were used with different manipulations of faculty “reminder prompting” using a four-phase design (Figure 8). The control class and the Experimental B class were prompted to use the Flipd App only during Phase I (first class), and the Experimental A group was prompted to use the Flipd App in Phase I (first class) and III (seventh class).

Figure 8.
An Experimental Assessment of Participant’s Flipd App Usage Through a Four-Phase (Dashed Vertical Lines) Experimental Design. The control class (white circles; n = 17) and the Experimental B class (gray squares; n = 34) were prompted to use the Flipd App only during Phase I, and the Experimental A group (black triangles; n = 10) was prompted to use the Flipd App in Phase I and III. The data show that with a “reminder prompt” following midterms (Phase III), participants increased their average Flipd App usage during the last 17 classes (71.67%) when compared with not receiving a “reminder prompt” (57.33%), a difference of 14.34%. The control class used the Flipd App 64% during this same time period. Thus, the data suggest that the Flipd App may be an effective distraction reducing technology but still requires the educational context of faculty prompting to be an effective educational technology intervention within the college classroom.

The data show that with a second “reminder prompt” following midterms (i.e., serving as a probe) during the seventh class (Phase III), participants increased their average Flipd App usage during the remaining 17 classes (71.67%) when compared with not receiving a “reminder prompt” (57.33%), which resulted in a reduction of cellular phone distractions within the classroom of 14.34%. The control class used the Flipd App 64% during this same time period. Thus, the data suggest that the Flipd App may be an effective distraction reducing tool but still required ongoing monitoring and feedback by faculty. Further, this indicates that faculty and the classroom environment are important educational contextual cues that students will respond to certain classroom management strategies that may not be as salient within an online classroom environment. Thus, this suggests that in the context of faculty prompting students within the college classroom in combination with the Flipd App may enhance the effectiveness of the Flip App as an educational intervention tool directed at reducing cellular phone distractions.

Discussion

At all levels of the educational system, classroom management strategies have been an essential part in establishing discipline, control, and shaping appropriate classroom behaviors directed toward improving student learning outcomes, active learning, and cogent discussions (Little & Akin-Little, 2008) that would result in increased college student retention. However, at the undergraduate level, cellular phone distractions within colleges are directly competing with student’s attention and their ability to actively engage in discussions that specifically serve students by enriching their learning experience. It is argued that reducing cellular phone distractions within the college classroom can serve as an initial opportunity to begin reshaping important academic behaviors through habit-reversal trainings (i.e., reducing cellular phone usage and increasing note-taking and hand-raising behaviors, respectively) to encourage more opportunities for active learning (Friedman, 2014). This may seem obvious, but less independently motivated student note-taking may require more faculty encouragement to take notes in class, and this may serve as a critical teaching prompt that may begin to recompete for student attention to the course curriculum within the college classroom. Further, this could be the critical habit to be reinstituted to have students retake ownership of their education to decrease attrition rates. Therefore, it can be argued that note-taking is an underexplored potential attrition risk factor that requires further study. Thus, faculty and students share an equal responsibility in engaging with one another through lecture to have and facilitate enriched educational discussions.

In addition, student attendance is an important component of any curriculum as attending lectures (i.e., whether online or in person) adds precious supplemental resources, tips, explanations, demonstrations, practicing content, and so forth to address any academic learning gaps that students may face when trying to independently learn the course material. When students do not attend class, they are at increased risk of attrition in the course. In the present study, the Flipd App usage was shown to have a positive linear relationship with attendance and a negative linear relationship with absences. Thus, the Flipd App may be an effective educational intervention tool that can reduce student’s cellular phone distractions within the college classroom and increase class attendance rates. When making 10% of the college course grade based on attendance (5%) and Flipd App usage (5%), it can be a fair incentive for students to maintain higher attendance rates and increase their opportunities to engage in enriched lectures with faculty. The present study determined that 40% of seniors and ∼31% of juniors used the Flipd App consistently (Figure 4). Although the current study only examined junior- and senior-level students, it would be important to examine how freshman and sophomore student attendance and absence rates would be influenced by the Flipd App. This is important as freshman may have the hardest transition into college and may have challenging transitions from refraining from cellular phone usage during class (i.e., as most national high schools restrict or lock up cellular phones and colleges do not). Because the present study only examined junior- and senior-level students, it was observed that even at a more responsible upper division level, undergraduate students were still compelled to use their cellular phones inconsistently and may benefit greatly from educational interventions tools such as the Flipd App (i.e., ∼10.00% of seniors compared with 58% of juniors). Thus, the Flipd App serves to make college students more self-aware of their cellular phone distractions and to encourage active self-monitoring strategies to make the necessary behavioral changes in engaging in better “screen-time” habits. “In 2017 [the Flipd App] had helped students spend 35 million minutes unplugged from their cellular phones” (Cristian Villamarin, CEO and Founder of Flipd. Inc., personal communication, 1 July, 2017). Thus, the Flipd App has effectively helped students to stay off their cellular phones 69.05 days during the 2017 year. To estimate the influences of the Flipd App on college students, assuming 8 months of the year students were in classes (i.e., ∼240 school days) and then divide the number of days “Flipd Off” by the number of school days and finally multiply that value by 100 (69.05/240100), the result would estimate a 28.77% decrease in cellular phone distractions while promoting an equal potential for increased note-taking and engaging in enriched lecture discussions. Whether this decrease in cellular phone distractions would correlate one-to-one with student retention remains to be elucidated. However, this initial report offers some promising data that this relationship could very well occur and technologies such as the Flipd App may prove useful as an educational intervention tool.

Notably, these estimates were not far off from the data obtained in the present study whereby 60% of the Flipd App usage data were positively correlated with student attendance rates and 58.7% of Flipd App usage data were negatively correlated with student absence rates. However, even when students attend classes, the data in Figure 5 suggested that in classes that required less or no hands-on activity as in a mathematical required course, the Flipd App may be useful in reducing cellular phone distractions while in the college classroom. This finding supports the claim of Friedman (2014) in faculty and students making more regularly occurring conscious efforts to support and encourage note-taking habit-reversal behaviors to compete with the compulsion to be distracted by cellular phones while in the college classroom. Consistent with Friedman’s (2014) claim, the experimental manipulation across classes (Figure 8) did in fact corroborate and showed that with a “reminder prompt” following midterms (Phase III), students increased their average Flipd App usage during the last 17 classes (71.67%) when compared with not receiving a “reminder prompt” (57.33%), a difference of 14.34%. In contrast, the control class used the Flipd App 64% during this same time period. Thus, the data suggest that the Flipd App may be an effective cellular phone distraction reducing technology but still requires (a) the context of an educational classroom (i.e., environmental control), (b) the faculty to provide prompting to be an effective tool within the college learning environment (i.e., stimulus control and rule governance), and (c) students and faculty to engage in lectures and encourage active note-taking within the college classroom (i.e., enriched discussions).

Moreover, the requirement of the incentive salient stimuli of (a) the faculty prompting (i.e., motivational component to learn and reinforce learning) and (b) the context of the educational setting (i.e., classroom environment) poses a unique set of issues with online-learning classrooms. Given the observations from the present study, perhaps the Flipd App may not be well suited to address an online learning environment in the same manner as what was observed in a traditional college lecture. However, the present study’s findings suggest that students still require self-accountability for their time management skills and priorities (i.e., as ∼10% of the sample would prefer to be on their cellular phone while in class than actually attending to and engaging in an enriched college lecture). Students have the choice to attend lectures, and perhaps the frame should be recontextualized by faculty and colleges to help students build more professional “job-market readiness” behaviors (i.e., punctuality, peer-to-peer interactions, active engagement in lectures, and taking ownership of their own course work) that may best prepare them for future careers. This latter point is directly related to attendance and absence rates, which may be perhaps more critical predictors of college attrition rates and career success beyond earning a college/university degree. Consistent with the aforementioned, faculty are also accountable for encouraging students to engage in these same “job-market readiness” behaviors, by providing students with feedback based on the their attendance/absence rates. Another way of contextualizing the Flipd App would be to draw comparisons with the alcohol relapse literature. Cellular phone Apps can be used to record people’s drinking behaviors and predict the risk for a future drinking episode so that a mental health therapist and/or drug addiction counselor can help the App user understand their behaviors, the environment, setting events, and risks (for review, see Bishop, 2016). Similarly, the Flipd App can be used within the classroom environment to record their cellular phone usage behaviors and predict when students may be at risk for becoming distracted (i.e., due to social media addictions and FOMA) and tune out of the lecture. In this way, through a concerted effort by both students and faculty, in conjunction with the Flipd App technology, the next generation of college students may be less distracted by their cellular phones and more inclined to engage in the active learning classroom environment to benefit from enriched discussions and the JOMO.

Conclusion

Increasing college student attendance and active learning within the classroom remains a challenge for faculty as cellular phone distractions and for some cellular phone addictions can directly compete with student’s attention and their opportunities to participate with faculty through lectures that may result in student attrition. The Flipd App serves as an effective proactive educational intervention tool that tries to leverage a more reasonable balance between students’ perceived need to use their cellular phones and their responsibility to learn within the active educational environment, which ultimately may increase student retention. The low-cost Flipd App (i.e., with packages ranging from $1.99 to $5.99 per month or ∼$24–$72 per year) also increases the affordability for many students across a broad range of socioeconomic factors and financial situations to purchase it. Further, the tracking systems within the Flipd App help students to self-monitor their own cellular phone usage versus active class participation times/cellular phone addictive/FOMA behaviors and, if need be, can prompt students to “Flipd Off” for an upcoming lecture. This is a critical point to emphasize, as Apps that contain real-time user feedback in the form of digital data have an increased likelihood of changing behaviors consistent with the learning and behavior principles of contiguity. Further, this is similar to social media and web solicitation Apps promoting consumers of new products, updates, and information; the Flipd App prompts students to be aware of their past cellular phone use behaviors and to be more conscious when going to class and to be more prepared for their courses. Faculty can also autoprogram all class days and times to help alleviate any students from “miss-programming failures*” that would otherwise be counterintuitive to such an educational technology intervention. Although this is the first study on the effectiveness of the Flipd App, the data provide beginning support that the Flipd App may be an effective proactive intervention educational tool within the college classroom. Further, the Flipd App reduces cellular phone distractions with promise directed toward reducing student attrition and increasing student retention as evidenced by its initial correlational outcomes on classroom attendance and absence rates. However, students still require faculty to prompt them to refrain from being distracted by their cellular phones while in class and to develop better competing habits to reverse these behaviors by taking active lecture notes, participating in active classroom discussions, and to help sustain their attention while in lectures they seek to enrich their college learning outcomes. Ultimately, the Flip App may be one of the first technologies that help the current generation of students that are compelled to remain connected to their cellular phones due to the FOMO, to begin to experience the world independent of their devices to experience the JOMO. Taken together, the Flipd App may be predictive of improved college attendance, more active lecture engagement, retention, graduation, and better quality knowledge and skills acquisition to generalize in the global workforce postgraduation.

Course	Class by semester (% of total sample N = 266)
PY3610 Brain & Behavior	28	10.53	22	8.27	N/A	N/A	34	12.78	N/A	N/A
PY3620 Drugs & Behavior	20	7.52	N/A	N/A	32	12.03	N/A	N/A	N/A	N/A
PY3120 Research Methods II: Statistics	17	6.39	N/A	N/A	16	6.01	N/A	N/A	N/A	N/A
PY4100 Research Methods III: Experimental Psychology	18	6.77	18	6.77	N/A	N/A	17	6.39	N/A	N/A
PY4401 Developmental Neuropathology	N/A	N/A	8	3.01	N/A	N/A	10	3.76	N/A	N/A
PY4402 Neuropsychopharmacology	N/A	N/A	N/A	N/A	20	7.52	N/A	N/A	N/A	N/A
PY5404 Environmental Neuroplasticity	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	6	2.25

Course and semester	Classroom characteristics
PY3610 Brain & Behavior
Fall 2016	86.47	82.35	5.88	70–100
Spring 2017	89.21	67.74	3.23	75.86–100
Fall 2017	N/A	N/A	N/A	N/A
Spring 2018	88.85	56.67	36.67	41.38–100
Summer 2018	N/A	N/A	N/A	N/A
PY3620 Drugs & Behavior
Fall 2016	89.33	70.00	10.00	76.67–100
Spring 2017	N/A	N/A	N/A	N/A
Fall 2017	93.10	46.67	36.67	78.57–100
Spring 2018	N/A	N/A	N/A	N/A
Summer 2018	N/A	N/A	N/A	N/A
PY3120 Research Methods II: Statistics
Fall 2016	90.00	64.71	11.77	76.67–100
Spring 2017	N/A	N/A	N/A	N/A
Fall 2017	91.77	50.00	22.00	66.67–100
Spring 2018	N/A	N/A	N/A	N/A
Summer 2018	N/A	N/A	N/A	N/A
PY4100 Research Methods III: Experimental Psychology
Fall 2016	86.49	73.68	10.53	66.67–100
Spring 2017	91.03	57.69	15.39	70.37–100
Fall 2017	N/A	N/A	N/A	N/A
Spring 2018	92.04	35.00	15.00	70.37–100
Summer 2018	N/A	N/A	N/A	N/A
PY4401 Developmental Neuropathology
Fall 2016	N/A	N/A	N/A	N/A
Spring 2017	86.42	75.00	18.75	65.52–100
Fall 2017	N/A	N/A	N/A	N/A
Spring 2018	91.17	60.00	10.00	82.14–100
Summer 2018	N/A	N/A	N/A	N/A
PY4402 Neuropsychopharmacology
Fall 2016	N/A	N/A	N/A	N/A
Spring 2017	N/A	N/A	N/A	N/A
Fall 2017	84.80	68.42	21.05	48.15–100
Spring 2018	N/A	N/A	N/A	N/A
Summer 2018	N/A	N/A	N/A	N/A
PY5404 Environmental Neuroplasticity
Fall 2016	N/A	N/A	N/A	N/A
Spring 2017	N/A	N/A	N/A	N/A
Fall 2017	N/A	N/A	N/A	N/A
Spring 2018	N/A	N/A	N/A	N/A
Summer 2018	99.17	0.00	83.33	95–100

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Lorenz S. Neuwirth

Author Biography

Lorenz S. Neuwirth is an Assistant Professor of Psychology at the SUNY College at Old Westbury and a member of the SUNY Neuroscience Research Institute (SUNY-NRI). Dr. Neuwirth is a neuroscientist who studies developmental behavioral neurotoxicology and its relationship to developmental disabilities. He is a proponent of increasing underrepresented minority undergraduate student participation in both the physical and social sciences. He is a first generation minority male who has benefitted greatly from programs dedicated towards reducing and/or attempting to eliminate the academic achievement gap (i.e., STEP, C-STEP, LSAMP, and AGEP). He received his Ph.D. in Biology/Neuroscience at The CUNY Graduate Center in 2014. He completed a two year postdoctoral NIEHS fellowship in developmental neurotoxicology and molecular epigenetics at Thomas Jefferson University’s Department of Anatomy, Pathology, and Cell Biology in Philadelphia, PA before joining the faulty at SUNY-OW. His research is focuses on understanding the effects of environmental neurotoxicants and their relationship to dysexecutive functions as they related to goal-directed attention and memory dependent neurobehavioral disorders. His other research interests are directed towards using technology to increase more meaningful educational experiences in the classroom. In addition, he also works with technology to find unique ways to teach neuroscience and research methodology to a wide range of students.

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Course and semester	Classroom characteristics
Course and semester	AT %	≥ 3 %	N/A %	R %
PY3610 Brain & Behavior
Fall 2016	86.47	82.35	5.88	70–100
Spring 2017	89.21	67.74	3.23	75.86–100
Fall 2017	N/A	N/A	N/A	N/A
Spring 2018	88.85	56.67	36.67	41.38–100
Summer 2018	N/A	N/A	N/A	N/A
PY3620 Drugs & Behavior
Fall 2016	89.33	70.00	10.00	76.67–100
Spring 2017	N/A	N/A	N/A	N/A
Fall 2017	93.10	46.67	36.67	78.57–100
Spring 2018	N/A	N/A	N/A	N/A
Summer 2018	N/A	N/A	N/A	N/A
PY3120 Research Methods II: Statistics
Fall 2016	90.00	64.71	11.77	76.67–100
Spring 2017	N/A	N/A	N/A	N/A
Fall 2017	91.77	50.00	22.00	66.67–100
Spring 2018	N/A	N/A	N/A	N/A
Summer 2018	N/A	N/A	N/A	N/A
PY4100 Research Methods III: Experimental Psychology
Fall 2016	86.49	73.68	10.53	66.67–100
Spring 2017	91.03	57.69	15.39	70.37–100
Fall 2017	N/A	N/A	N/A	N/A
Spring 2018	92.04	35.00	15.00	70.37–100
Summer 2018	N/A	N/A	N/A	N/A
PY4401 Developmental Neuropathology
Fall 2016	N/A	N/A	N/A	N/A
Spring 2017	86.42	75.00	18.75	65.52–100
Fall 2017	N/A	N/A	N/A	N/A
Spring 2018	91.17	60.00	10.00	82.14–100
Summer 2018	N/A	N/A	N/A	N/A
PY4402 Neuropsychopharmacology
Fall 2016	N/A	N/A	N/A	N/A
Spring 2017	N/A	N/A	N/A	N/A
Fall 2017	84.80	68.42	21.05	48.15–100
Spring 2018	N/A	N/A	N/A	N/A
Summer 2018	N/A	N/A	N/A	N/A
PY5404 Environmental Neuroplasticity
Fall 2016	N/A	N/A	N/A	N/A
Spring 2017	N/A	N/A	N/A	N/A
Fall 2017	N/A	N/A	N/A	N/A
Spring 2018	N/A	N/A	N/A	N/A
Summer 2018	99.17	0.00	83.33	95–100