Kevin J
May, 2015
For Dr. Jeffrey Foley
Colorado State University
Author Note
This is a pilot study for research testing the feasibility of designing higher education around teaching students how to learn. To contribute to future study, see results, or view details on the program in development, please visit http://www.listenlovelead.com/education.
Abstract
Students that know how to learn demonstrate higher levels of academic performance. This pilot study tested 5 categories of learning fluency identified by the literature to discover their impact on student performance as measured by GPA. A selective sample of students in higher education completed a survey measuring demographic variables GPA and self-perception of proficiency in information, technology, interpersonal, intrapersonal, & academic fluency. Students reported that they did not necessarily receive direct training in learning fluency, but their GPA’s had improved overall (M=3.33) since the beginning of the program (M=2.375). Students with higher levels of learning fluency reported higher grades. However, a self-perception bias was present and a two-tailed independent samples t-test did not show statistically significant differences in the mean for the small number of samples (t(14) = 1.69, p = .11). Similarly, there was no statistically significant correlation between higher levels of learning fluency and greater amounts of change in GPA (r =0.18, p =.52) or current GPA (r =0.40, p =.12) . Even though they were not statistically significant at p <.05, the findings of this study align with the literature that reports a positive relationship between learning fluency and academic performance.
Keywords: higher education, learning fluency, achievement gap, academic performance, information literacy, curriculum design, academic mobility
Download Link – The Impact of Learning Fluency on the Acheivement Gap
Impact of Learning Fluency on the Achievement Gap
“There is a danger of a new elite developing in our country: the information elite” (ACRL, 1989). These words by Terence Bell, former US Secretary of Education accompanied the definition of information literacy (or information fluency) most commonly cited among research on the subject. According to the American Library Association, information literacy is a set of abilities requiring individuals to “recognize when information is needed and have the ability to locate, evaluate, and use effectively the needed information” (ACRL, 1989).
The same year this definition was published, the Internet was born and its development would eventually lead to redefining information literacy to include a technology component. Less than two decades later, studies conducted in Scotland, Singapore, and by UNESCO all recognize the importance of training students in both information literacy and in technology skills (Young, et al., n.d.; UNESCO, 2006; Mokhtar, Foo, & Majid, 2007). Though access to technology makes learning opportunities more accessible to students of multiple backgrounds (Schocken, 2012), not all of them are prepared to thrive in this self-directed environment (Rienties, B., Giesbers, B., Tempelaar, D., Lygo-Baker, S., Segers, M., & Gijselaers, W., 2012).
Students need more than just information and technology fluency to succeed in today’s learning context. Richardson et al. (2012) conducted a study associating various psychological attributes to student performance. Other studies have identified factors like motivation (Codreanu & Vasilescu, 2013), creativity (Bloom, 1956), critical thinking (Brookfield, 2013), and many other factors as crucial to student success in learning. Some researchers group all these aspects under the heading of information fluency but others identify them separately. The following literature review outlines five distinct aspects of learning that are considered vital to student performance. Mastery of these learning skills plays such an important role in student success that some have suggested including information fluency (or learning fluency) as its own discipline instead simply trying to incorporate its ideas into the general curriculum of a school (Mokhtar, Foo, & Majid, 2007; Virkus, 2003).
The Research Problem
Although many studies show that teaching students how to learn can have a positive impact on their performance, it is nearly impossible to find a study that references the impact of learning fluency on the achievement gap. Maria Montessori observed that her mentally challenged students were able to outperform the public school students after experiencing her education system, but assumed that this improvement would be more significant for students that were not mentally challenged (Montessori, 2004). A more recent study by Chatterall, Chapleau, & Iwanga showed a dramatic increase in academic performance among students with low economic status when they were given musical training (as cited in Sousa, 2011). However, Sousa did not report whether this was compared with the performance of students in other economic brackets. There has been no correlation between training students in aspects of information fluency and a subsequent reduction in the gap between high and low performing students.
Justification of the Problem
A reduction in the achievement gap could be identified through academic mobility. Although the term has been hijacked to refer to students moving from one school district to another, its meaning should be similar to that of social mobility: students moving from a less-desirable academic status to a more desirable one. According to the theory of multiple intelligences (Gardner, 1995), this mobility should be taking place naturally as some students are more competent at certain subjects. However, the only way to achieve a 4.0 is to be good at every class – or perhaps, as Dewey suggested, to be part of a system that is “suitable” to one’s learning needs (Dewey, 1938, p. 45). Student-centric learning has attempted to make the classroom more suitable to a diversity of students, but research on learning fluencies suggests that training students to thrive in a diversity of environments may be a more effective alternative. Richardson et al. (2012) have shown that while some aspects of an individual are more “stable” or unchanging, other aspects have great potential for development. This suggests that the skills required for academic mobility can be taught.
Purpose Statement
The purpose of this study is to identify whether training students how to learn can lead to a reduction in the achievement gap. This is done by examining the relationship between self-reported proficiency in the five learning fluencies and changes in academic performance for students at a local higher education institution that offers some aspect of training in learning fluency. The relationship between these two factors could indicate whether shifting the function of formal education toward an emphasis on developing students’ learning abilities could lead to an improvement in student performance and academic mobility, thereby narrowing the achievement gap.
Research Questions and Personal Interest
An overview of the literature outlines the various factors that other studies have identified as aspects of learning fluency. This study tests the correlation between these factors and student performance as measured by GPA. Despite some downsides, no better indicator has yet been found to replace GPA as a measure of student performance (Richardson, et al., 2012). This study compares self-identified learning fluency levels with GPA scores to see if higher levels of fluency are related to higher GPA’s or greater amounts of change in GPA. If the relationship is significant, the author has a personal interest in further study to see whether student mastery of a subject may be inferred by measuring student mastery of learning fluencies.
Audiences that will Benefit from the Study
This study identifies the potential benefits of devoting classroom time to developing student learning fluency. The results are relevant to school administrators and policy makers who must decide between incorporating learning fluency into the curriculum or offering it as an independent subject of study. Remedial program coordinators and curriculum designers may find the insights useful in focusing their strategies on those aspects of learning that provide the greatest return on investment. Teachers may find this study useful in designing assessments and opportunities for students to improve. Professional students and life-long learners that want to improve their academic performance may find it helpful to understand the role that learning fluency can play toward this end. Finally, the learning centers that participate in this study and future research will gain insight into what their students perceive to be the most valuable differentiating factors that the school might contribute to their academic success.
Literature Review
A review of the literature has identified multiple factors of learning that can be classified into five different categories: information, technology, interpersonal, intrapersonal, and academic fluency. These five categories of learning fluency have been shown to have an impact on student success. Not every student has the same level of capability in these fluencies, but according to Sousa (2011), at least some of the fluencies can be developed. Candy (2006) demonstrated the importance of this intentional development by saying that the information explosion has made self-directed learning a required skill that cannot simply be trusted as an accidental byproduct of education. Although each field of learning has its own particular requirements, the skills commonly required by the self-directed life-long learner guided the selection of factors in this study.
Information Fluency
Within the literature, these skills are sometimes identified under a broad category called “information literacy.” The American Library Association developed the most commonly used definition of information literacy in 1989: “Information literacy is a set of abilities requiring individuals to “recognize when information is needed and have the ability to locate, evaluate, and use effectively the needed information” (ALA, 2000). Virkus (2003) identified multiple terms that are often substituted for information literacy and range from very narrow to very broad definitions. For purposes of this study, information literacy is limited to the definition provided by the ALA and is measured through concrete skills like reading comprehension, reading speed, mastery of research methods, and skill in evaluating resources.
Technology Fluency
Recent studies by UNESCO have called for an expansion of the definition of literacy to include a plethora of learning skills, but most importantly those related to technology (UNESCO, 2006). According to the International Society for Technology in Education (2015), developments in technology have changed how we learn. Today, students need to learn how to use the computer before they can use the computer to learn about something else. Mokhtar, Majid, & Foo (2008) argued that despite the technical ability of modern students, training is still required for students to use technology for educational purposes. Many students know how to use Facebook and search engines, but this does not automatically translate into fluency with the creation and exchange of information through databases (Virkus, 2003). Common examples of technology fluency are website design, basic IT skills, use of academic databases, MS Office proficiency, graphic design experience, and familiarity with communication technology.
Interpersonal Fluency
Among constructivist circles, interpersonal fluency plays a very important role in learning. A study by Andretta (2007) showed that using a relational approach to learning, and teaching students how to learn through information literacy could provide a framework for life-long learning. Learning methods imported form ancient Greece include the trifecta of logic, rhetoric, and dialectic, which was dominated by interpersonal communication skills. The ALA identifies the continuing importance of these skills by highlighting the importance of fluency in speaking, debating, writing, asking questions, presenting, and communicating across cultures (2000).
Intrapersonal Fluency
Merriam & Bierrema (2014) in their textbook, Adult Learning: Linking Theory and Practice identified multiple aspects of intra-personal learning in their overview of educational theories. Chief among these are critical reflection, personal experience, physical and spiritual aspects of learning, motivation, critical thinking, and cultural understanding. These are not untested ideas. They are important dynamics of the major learning theories. However, for some reason, literature directly testing the impact of training students in these aspects of learning was not readily available. Many teachers are aware of the impact of learning styles, personality types, and the external environment on student learning, yet their classroom time is often limited to presenting information and not to teaching students how to manage the learning process more effectively (Palmer, 2007).
Academic Fluency
A fifth aspect of learning fluency identified from the literature is specifically academic. Huvila (2011) declared that ideas of information literacy were not complete without first helping students develop skills in the creation and organization of information. Sousa (2011) suggested using Bloom’s Taxonomy as a guide to moving students toward higher levels of learning. Developing a study plan, applying ideas (Dewey, 1938), observation skills (Montessori, 2004), innovation, creative thinking (Bloom, 1956), and more fall into this category. Keen (as cited by Virkus, 2003) has identified academic competencies like these to be transferrable across multiple environments. Academic fluency can be summarized as the ability to apply the previous four learning fluencies in a specifically educational context.
Deficiencies in the Literature
Although the five learning fluencies are fairly well developed through the literature (for a full synthesis of this research, please see Jenson, 2015), research on their application has almost exclusively focused on their integration into the teaching of subject material. The ALA recommended teaching these skills by incorporating them into multiple subjects rather than on their own (ALA, 2000). More recently, Mokhtar, Foo, & Majid (2007) recommended creating a new position for a teacher-librarian whose responsibility is to educate students on information technology and literacy skills. However, the researcher could find no study testing the impact of teaching all five learning fluencies as a stand-alone subject to equip students for self-directed life-long learning. Studies have been conducted to show that one or another aspects of learning have an effect upon student performance, but none that explore their combined impact on the achievement gap.
Research Methods
Data for this quantitative study was collected through the use of a survey and analyzed using Microsoft Excel and SPSS. The objective of the research was to discover the relationship between self-reported levels of learning fluency (as defined by the literature) and changes in GPA between high school, first term, and current term. Change in GPA was calculated by taking the difference between current GPA and either high school or first term GPA. Scores were calculated for each of the five learning fluencies and these were added together to calculate the students learning fluency score. Change in GPA, current GPA, and the learning fluency score were analyzed for each student. High levels of change in GPA or a reduction of variance in GPA would indicate a reduction in the achievement gap and this would be related to self-reported levels of learning fluency.
Data Collection Instrument
To collect data for analysis, a survey was designed specifically for this pilot study, which included multiple variables for use in future research. The first part of the anonymous survey form covered demographic information, information on previous educational experience, academic performance, and several influencing variables that can be used in more advanced statistical analysis in follow-up studies. A variety of question types were included and students were asked to submit rational data wherever possible. The entire survey is included in the appendix.
The second part of the survey asked students about their familiarity with various aspects of the five learning fluencies from the literature review. Questions from each area of learning fluency were kept together rather than scattered in order to develop a general sense of student ability in each category. Students rated their agreement with statements by using the following Likert scale. They also had the option of circling S or T to indicate their opinions on the importance and accessibility of each skill. These are the instructions they received on the survey form.
Please indicate how much you agree or disagree with the statements on the next page by circling a number on the following scale:
Completely Disagree 1 2 3 4 5 Completely Agree
ALSO circle S and/or T whenever appropriate.
S = I believe this skill is important to my SUCCESS as a student
T = my school offers TRAINING on this skill
Example: I enjoy taking surveys! 1 2 3 4 5 S T
Student enjoys taking surveys, does not believe this skill to be important to success as a student, but is aware of training offered by her school.
Validity and Reliability of the Instrument
Because this instrument was new, steps were taken to insure its validity and reliability (Creswell, 2015). Survey questions were reviewed for clarity and phrased for consistency. For example, students who believed themselves proficient in a skill would always indicate this by selecting a number closer to 5. Specific data was preferred over general data and all but one question had a predefined range of answers. The procedures for administering the survey were the same for every student and students had access to the researcher for any questions while completing the survey.
Measures of validity were built into the design of the survey. For example, students who identified their learning style on the front completely agreed that they knew their learning style when asked about this on the back. Those who did not identify a learning style indicated no agreement or partial agreement. A visual overview of the data showed that student responses within each category tended to vary less than they did between categories. If a student self identified a low level of proficiency in one aspect of interpersonal fluency, most aspects of interpersonal fluency would also be low. This consistency demonstrates the validity of the questions to examine each category of learning fluency.
Another measure of validity comes from the difference in agreement levels for objective measurements compared with subjective measurements. An average of 9.5 students agreed over whether training was offered or not (an objective measure) and 8.1 agreed about the importance of that measure to their success as a student (a subjective measure). The objective measure had less of a range of opinions showing that the survey was able to quantify student opinions somewhat effectively.
Survey Administration
The target population for the survey consisted of students not in their first term of study at an intensive liberal arts program at a local school. This target was selected on the basis of three factors. First, personal interviews with the staff identified a significant change in performance for disadvantaged students. Second, students in this program are admitted on variables besides GPA providing a variety of GPA starting points to analyze. Finally, students in the target population complete the program of study with the same materials, order of presentation, classmates, professors, and learning environment. This provided control for the influence of confounding variables like class size, instructional methods, and subject of study.
The data collection instrument was administered during class time by permission of the school and oversight of the professors. Students received an overview of the research project, guidance on how to answer the questions correctly, and a disclaimer that they did not need to participate or complete any questions if they did not feel comfortable doing so. The population of students was stratified into three groups based on their time in the program and one group was selected for the data collection process. Students in their final term (Group 3) were not available and students in their first term (Group 1) would not be able to provide data to measure the change in GPA. For this reason, Group 2 was chosen to provide the sample data.
From these students, 16 surveys were collected. This number is within the sample range recommended by Creswell for an experimental study, but is below the 30 recommended for a correlational study (2015). If the study had been designed as an experimental study with a pre-test, post-test, and verified training in the measured aspects of learning fluency, the sample size may have been appropriate. However, there was only one point of data collection and students were unsure of whether training was available in learning fluency, so the number of samples was too small. However, no additional students were available that met the criteria for participation.
Results
Demographic Data
Sixteen students participated in the survey. There were 10 males and 6 females. Fifteen out of the sixteen were between age 18 and 21. They had an average of 1.4 years of higher education experience including the 1 year they had completed as part of their current program. Most students did not know their learning styles, but the four who did reported as kinesthetic, visual, and two aural learners. Three attended public school, six attended private school, three were homeschooled, and four attended a combination of these for their high school experience. For half of them high school was a challenge and all but one student had families who believed in the importance of academics. Future study interests ranged from music, liberal arts, dancing, writing and psychology to business, law enforcement, sports, architecture, and the biological, architectural, and actuarial sciences. All but four wanted to see the results of this research.
GPA Data
Most important for purpose of this study, student GPAs were reported for high school, the first term of higher education, and the current term to date (Table 1). Data from the table shows that average GPA declined dramatically in the students’ first year and began to rise to its current level. Because GPA is capped at 4.0 the highest scores for each group did not change. Although the range of reported GPA’s did not change between the first year and current year, the total variance in GPA scores was smaller. Combined with the rise in overall GPA, this indicates that a greater number of students were beginning to achieve higher scores – a reduction in the achievement gap between high and low scoring students. When compared with high school GPA data, this reduction is not significant, but it also accounts for a much greater range of scores because of an outlying GPA of 1.8.
| Table 1 | |||
| GPA Data | |||
| Group | High School | 1st Year | Current |
| GPA Average | 3.459 | 2.375 | 3.331 |
| Low | 2.8 | 1.8 | 1.8 |
| High | 4 | 4 | 4 |
| Range | 1.2 | 2.2 | 2.2 |
| Standard Deviation | 0.427 | 0.667 | 0.621 |
| Variance | 0.182 | 0.444 | 0.384 |
Learning Fluency Data Overview
Along with GPA, learning fluency data was the most important aspect of the survey. Students were asked three questions about their perspectives on learning fluency. The first measured their proficiency, the second measured their beliefs about its importance to success in learning, the third measured whether they believed their institution offered training in the subject. The last two provided reliability testing for the data collection instrument as well as a few insights into student perceptions of their institution.
Training data. Students tended to disagree over whether or not the school offered training in academic fluency with 6-8 students voting either way in the true/false test. However, they agreed that there was little or no training provided in technology fluency (10-16 students for each factor). On average 9.5 students agreed over whether any given factor was taught or not. Students were more likely to agree that training was not available for any randomly selected factor. This led to an overall average of 37% agreement that training was offered for a given variable. Where training was not offered, students tended to believe the skill was less important to their success in the program.
Success data. Out of the 18 factors ranked most important to student success, only 5 had little or no training offered by the school. These factors were listening skills, learning style, eating habits, motivational skills, and the use of MS Office. On the other hand, for every fluency factor except two, students were more likely to believe a factor was important to their success (S) than to believe their institution offered training (T).
This is significant because students scored lower on subjective vs. objective measurements in the control questions. The importance of a factor to student success is a subjective opinion and should therefore have been lower than students’ beliefs about whether training was offered. The exceptions to this were debate and speech, which are key parts of the learning experience at this institution. For these, students were more likely to agree that training was offered than to agree over its significance.
Fluency score. The average fluency score for students was calculated at 118. This was done by adding together the ranks (from 1-5) students gave to each factor of learning fluency. If students had completely disagreed with all statements of proficiency, they would have scored a 35. If they had completely agreed with every statement of their proficiency, they would have scored a 175. Actual student scores ranged from 94 to 155. The range was 61 with a variance (Excel: Var.S) of 240.8 and standard deviation of 15.
Sub-scores for each category were calculated but are invalid because the number of factors in each category was different. Instead of calculating an average for each section by adding the factors together, the average proficiency rank for each category was calculated for every student. The average proficiency identification for students within each category is shown by Figure 1. Students ranked themselves above average for every category except technology. The low score in this category has a distinct impact on the average student score.
Students who disagreed with statements of their proficiency received a score of 1. Those who completely agreed with proficiency statements received a score of 5. The first question students answered measured perception of their ability in taking surveys. On average, students rated their survey taking ability at 3.6, which is slightly higher than the average rank they gave to their fluencies (3.4). Students with lower GPA’s rated their survey taking ability lower (2.6) than students with higher GPA’s (4.0) indicating that grades may have influenced self-perception of ability.
Table 2 shows that students in this class believe they are academically proficient. They do not believe they are proficient in technology, but as noted previously, they do not receive training in this area or believe it to be significant to their success in the program. Interpersonal ranking showed the least amount of variance in student selection (0.17) compared to the average variance of 0.47 (for all data from the table). Benchmarks for the variance are provided by the objective question about learning (variance = 0.16) and subjective question of survey taking ability (variance = 0.65).
| Table 2 | |||||
| Student Proficiency Rankings | |||||
| Factor | M | Sum | Range | Variance | SD |
| Surveys | 3.63 | 58 | 3 | 0.65 | 0.81 |
| Learn | 4.81 | 77 | 1 | 0.16 | 0.4 |
| Information Fluency | 3.58 | 57.2 | 2.8 | 0.63 | 0.8 |
| Technology Fluency | 2.46 | 39.43 | 3 | 0.65 | 0.8 |
| Interpersonal Fluency | 3.72 | 59.5 | 1.67 | 0.17 | 0.42 |
| Intrapersonal Fluency | 3.65 | 58.38 | 2.75 | 0.61 | 0.78 |
| Academic Fluency | 3.85 | 61.67 | 2 | 0.4 | 0.63 |
| Averages | 3.67 | 58.74 | 2.32 | 0.47 | 0.66 |
Comparison of the Mean t-Tests
On the basis of their proficiency scores, students were divided into two categories: those whose proficiency score met or exceeded the average of 118 and those whose score was below 118. Current GPA and change in GPA were measured for students in each category to see if there was a significant difference in the mean. The null hypothesis stated that there was no significance between the two groups, Ho: µA-µB = 0. The alternative hypothesis for each of these studies tested for a difference in the means, Ha: µA-µB ≠ 0.
The mean change in GPA for students with higher than average levels of fluency was .15 (SD = .14) and the mean change in GPA for students with lower than average levels of reported fluency was -0.5 (SD = .88). A two-tailed, independent samples t-test (Table 3) led to the conclusion that there was not enough evidence to suggest a difference between the two groups, t(7.3) = .645, p =.53. The amount of variance between the two groups was significant F(7.33) = .017. The same t-test led to the conclusion that there was no statistically significant difference in the mean of current GPA’s for students of the two groups t(14) = 1.69, p = .11, though the amount of variance was not significant. It is important to note that fluency scores had a stronger relationship to current GPA (t =1.69, p =.11) than to 1st term GPA (t =1.53, p =.15) or high school GPA (t =1.15, p =.27).
A second t-test compared the mean fluency score and GPA for students grouped by positive or negative change in GPA. No significant differences were discovered, but the results are shown in Table 4. Additional analysis of these groups relative to the categories of learning fluency also failed to produce statistically significant results at p <.05. The greatest difference between the two groups was found in the ranking averages for interpersonal fluency (t = 1.24, p =.24). The least significant difference was found in the intrapersonal average rank and sum (t =-.29, p =.78).
Correlation Studies
Pearson Correlation showed a relationship of r =0.18 (p =.52) between the fluency score and the greatest change variables. The relationship between the fluency score and current GPA was stronger and more significant (r =0.40, p =.12). This is consistent with the literature from which the variables were drawn that demonstrates a relationship between GPA and learning fluency. Figure 2 shows a scatterplot of the samples for this second test of correlation.
A correlational matrix (Table 5) of the factors that make up the total fluency score revealed a strong relationship between technology fluency and interpersonal fluency (r =.505, p =.05) and between academic fluency and intrapersonal fluency (r =.700, p =.01). Since there were very few statistically significant findings in this study, these relationships are unique exceptions.
The most significant correlates to change in GPA were current GPA (r =.627, p =.01), academic fluency average (r =30, p =.26), and interpersonal fluency average (r =.15, p =.52). Current GPA was the only statistically significant correlate at p <.05. The most significant correlates to current GPA were change in GPA (r =.627, p =.01), academic fluency average (r =.45, p =.08), and intrapersonal fluency average (r =0.40, p =.13). Nearly all factors had a statistical significance at p <0.1. This indicates that there is a stronger correlation between the fluency factors and current GPA than between the fluency factors and change in GPA. It also suggests that the different categories of learning fluency may have different kinds of effects on student performance.
Discussion
The purpose of this study was to discover whether there was a relationship between learning fluency, as defined in the literature review, and change in GPA. A comparison of the mean GPA for students with higher and lower levels of fluency showed a relationship between student GPA and proficiency in the five learning fluencies that fell .02 short of statistical significance at the p <.1. This indicates the validity of the factors selected to measure learning fluency as it matches the findings of the literature.
Comparison of Means
Comparison of the means did not reveal any other significant differences. However, in every test, students with higher GPAs showed higher levels of learning fluency (see Figure 3) and students with higher levels of learning fluency showed higher GPAs. Similarly, students with higher levels of learning fluency showed greater amounts of change in GPA (.15, SD =.14) than students with lower levels of learning fluency (-0.5, SD =.88). However, the standard deviation and variance were so large that none of the results were statistically significant. In fact, the confidence intervals provided by the t-tests included the possibility of negative or positive change for students that reported higher levels of learning fluency: CIs [-0.131, 1.125] [-0.535 to 0.942] for current GPA and change in GPA respectively.
Correlational Studies
Pearson Correlation supported these findings by showing a positive relationship between change in GPA (r =0.18, p =.52) and current GPA (r =0.40, p =.12) relative to learning fluency. Once again, the relationship between learning fluency and change in GPA is almost statistically significant at the p <.10 level. Correlation also showed that academic fluency is the most significant correlate to GPA for students in this particular program. This emphasized the idea that the particular program that students are part of and the emphasis of their training may have a significant effect on which factors of learning fluency students are exposed to.
Significance of the Results
Central Tendency indicates that as the sample size increases, the standard deviation and variance of the mean should decrease for a population with standard normal distribution curve. The sample size for this study was so low that the variance and standard deviation kept the statistical test from producing significant results, even at the p <.10 level. This led to a rejection of the alternate hypothesis that there was a difference between students with high or low GPA’s and high or low fluency scores (Ha: µA-µB ≠ 0). As the results showed, there was a difference, but it was not statistically significant. With a larger sample size and a more controlled environment, it is quite possible that researchers would find a statistically significant difference between the groups and accept the alternative hypothesis.
In addition to sample size, there are several other indications that this could be the case. First, students were sampled in their second term. Grades in the first term had declined dramatically since high school, but had begun to improve. This indicates that many students were not prepared for the difficulty of the program. However, by the end of second term when the data was collected, students had become familiar with the skills needed for success in that environment and GPA had begun to rise (see Table 1 for details). More importantly, the variance had declined from 0.44 to 0.38 and the standard deviation was smaller. If this trend continued, one could assume that student grades near the end of the third term would be significantly less distributed than after the first term – indicating a reduction in the achievement gap.
Second, the lowest GPA reported was an outlier and so was the student who reported the greatest negative change in performance (see Figure 2). T-tests are sensitive to these outliers and may have shown more statistically significant differences of the mean if they had been removed. Additional samples would also reduce the impact of these outliers.
Third, self-reported fluency levels were influenced by student self-perception. This was indicated by the control questions about survey taking ability and the availability of training in learning fluency. The researcher did not have adequate statistical skills to account for this influence. However, to control for it entirely would require an experimental design and a larger sample size.
Influencing Factors
The self-perception bias limited the scope of the study to discovering whether a relationship existed between student grades and student perception of their learning ability. It is impossible to say whether student self-perception of ability influenced their GPA scores or whether the GPA’s gave the students more or less confidence in their ability. It is possible that this bias also influenced the correlational studies to show a stronger association between learning fluency and current grades than between learning fluency and change in GPA.
Out of all the learning fluency factors, academic fluency had the greatest amount of relationship with change in GPA and current GPA. Since the curriculum of the school is largely designed around proficiency in these skills, it is possible that the lack of such skills could account for the drop in GPA for students in their first term, and its subsequent rise in the second term. Relative to this particular program, training in academic fluency skills may be vital to reducing the achievement gap.
Correlation between the various fluency factors (see Table 5) showed that students with higher levels of academic fluency also showed higher levels of intrapersonal fluency. Intrapersonal fluency questions were not limited to cognitive skills like memory and critical thinking, but included aspects of lifestyle like exercise, food choices, and emotional development. This indicates that in order to develop academic fluency among their students, schools may need to help students develop healthy lifestyle habits.
The other two factors of learning fluency that were strongly correlated were interpersonal fluency and technology fluency. This correlation may be a result of students using technology for the purpose of communication. Those with lower levels of interpersonal skills may have fewer applications for technology. This theory is supported by the lack of correlation between information fluency and technology fluency – which were shown by the literature to have a significant relationship. Because the students are not offered training in technology, they may not be aware of the benefits it can bring to their learning experience.
Most Significant Finding
With the exception of correlation between factors, the strongest relationship in this study was found between learning fluency and current grades (t(14) =1.69, p =.11; r =0.40, p =.12) This makes sense in light of the literature review. Factors for analysis in this study were chosen from research reports that demonstrated their relationship with student performance. Those same factors were used in this study, but in combination rather than as individual factors. Additionally, the environment in which these factors were tested was not the same as the environment in which the factors were originally studied. Thus, the results of retesting these factors should have been close to statistically significant, like they were in this study. As explored in the limitations section, this relationship was the only one that this study could effectively measure and it came close to statistical significance despite the small number of samples.
Limitations
Correlational studies showed that the availability of training and the type of program students are in may have an impact on their mastery of various learning fluency factors. For this reason, the selective sampling method used in this study limits the findings of this study to students within the particular cohort that completed the survey. The results cannot necessarily be applied to students outside of the particular program studied or even students who did not experience the same environment as the selected sample group. At least one additional study of students in a different environment is needed to generalize these findings to the broader population of students in higher education.
Data issues. Issues with the data included two students that did not contribute their current GPA and two that did not contribute their first term GPA. In at least one instance, the GPA was unknown to the student. To account for the missing data, current GPA was assumed to be the same as the last known reported GPA.
Additional problems with the GPA data include one outlier whose academic performance dropped significantly between high school and higher education and the 37% of students who showed no change in GPA between first term and current term. To account for this missing or unhelpful data, change was calculated between high school and current GPA and between first term and current GPA. Instead of testing both differences, the greatest of these was selected from each student to create a new category called “greatest change.” These changes allowed for tests to be run on 16 instead of 12 sets of data, however they also reduced the depth of analysis that could have been done with two measurements of change in GPA.
Sampling. Although the students who were sampled experienced a controlled environment, they were only 37% confident that training was available for any randomly selected factor of learning fluency. For this reason, it is impossible to tell whether self-reported mastery of learning fluency was the result of innate capacity or training from participation the program. The rise in GPA between the 1st and current term may be a result of factors besides the development of learning fluency. To control for this, the sampling and research methods will need to change.
Research methods. The purpose of the study was to measure change in academic performance (GPA) relative to change in student proficiency in the five learning fluencies. Because the survey only took place at one point in time, it provided a snapshot of data that could not measure the change in student perceptions of proficiency in learning fluency. This meant that the study could measure the relationship between GPA and current learning fluency, but not between GPA and changes in learning fluency. Any relationship between changes in learning fluency and academic mobility could only be implied. Additional studies will be necessary to determine if helping students improve their learning fluency will lead to a decrease in the achievement gap.
Research Revisions and Recommendations
Based on the findings and limitations of this pilot study, it is possible to redesign the research methods of this study to produce statistically significant results that apply to a broader population and demonstrate whether training in academic fluency can lead to an increase in academic mobility. The sample for such a study should be randomly selected from a variety of classrooms, ages, subjects, and programs so that the results are more broadly applicable. The selected students and those who are not selected will receive a pre-test to establish a baseline of learning fluency and academic performance. Then selected students receive training in the five learning fluencies as a supplement to their regular program. The literature review suggested that training has been shown to improve student performance, however the type of training has not been specified. To account for this, a control group will receive supplemental training in study and test-taking skills. Students who are not selected for either training continue their regular program of study. After the training is complete, a post-test survey will be administered to students of all three groups to compare changes in learning fluency and changes in GPA.
Because the experience of the students will controlled, the relationship between learning fluency and change in GPA can be measured on the basis of training received and not on the basis of students’ self-perceived learning fluency. This controls for the self-perception bias and provides a stronger conclusion as to the effectiveness of training students how to learn. Students will still report their levels of fluency to evaluate the effectiveness of their training and to compare changes in learning fluency between the three groups.
Training details. In order to make this sort of training accessible to students, a pedagogy must be developed that not only defines the learning fluencies, but offers suggestions of how to help students develop their proficiency with them. As the drop in student performance between high school and college indicated in this study, students will be at different starting points in their development of learning fluency. Not only does this pedagogy need to identify the elemental starting points of academic fluency, but it should also provide the scaffolding needed for students to develop their skills to the point where they no longer need the teacher (Jenson, 2015).
Some studies have suggested that schools offer this training directly, while others advocate integrating various learning fluencies into the standard curriculum. Regardless of the method chosen studies like the report by Mokhtar, Majid, & Foo (2008) have showed that students require ongoing coaching from a learning expert in order to develop the skills they need for effective learning. Long-range studies are also needed to discover whether an integrated training program is more effective than a direct training program for students to develop learning fluency.
Program analysis. If a relationship between training in learning fluency and an increase in academic mobility is established, it will be possible to measure the effectiveness of educational programs using these factors. If a school has a significantly higher percentage of students who underperformed in high school, but now have high grades and there is a significant correlation between grades and learning fluency, then it will be possible to infer that the school does an effective job of helping students develop learning fluency. Levels of learning fluency can also be compared with the availability of training to see whether direct training in certain factors of learning fluency would be an effective supplement to existing programs.
Conclusion
Although this pilot study did not produce any statistically significant relationships, it did confirm the importance of continued research on the impact of learning fluency on the achievement gap. The insignificance of the findings in this study was largely an issue of sample size and experimental control. Instead of receiving training on all aspects of learning fluency, students on average reported the availability of training in a randomly selected learning factor 37%. Furthermore, the one-time survey was unable to measure whether training in learning fluency led to changes in student proficiency in learning fluency. Any relationship between improvements in learning fluency and improvements in GPA could only be implied and not directly measured.
Despite these limitations, there was still enough data to establish a weak relationship between the five learning fluencies and student performance. Comparison of the means and correlational studies consistently showed a positive relationship between higher levels of learning fluency and higher levels of change or current GPA. For current GPA, the results barely fell short of reaching statistical significance at p <.10. This makes sense given the indications of the literature that there should be a relationship between the fluency factors and student performance.
These results were not statistically significant, but they provide a starting point for future research that controls for student self-perception bias and indicates the effectiveness of training in learning fluency to increase student mobility. In this study, student mobility was defined as students moving from a less desirable to a more desirable level of performance – a reduction in the achievement gap. If improvements in learning fluency can be linked to this kind of change, it will be advisable to redesign the educational experience to help all students develop their proficiency in the 5 learning fluencies.
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Appendix: Data Collection Instrument
Human-Centered Learning 