2014 Student Readiness Report

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INTRODUCTION

According to the 2013 Survey of Online Learning conducted by the Babson Survey Research Group over 7.1 million students were taking at least one online course during 2013. The 6.1% growth rate represents over 400,000 additional students taking at least one online course. Seventy-four percent of academic leaders rate the learning outcomes in online education as the same or superior to those in face-to-face courses. Sixty-nine percent of higher education institutions say that online learning is a critical part of their long-term strategy.

In addition to the increased numbers of online courses, many institutions are now providing hybrid courses which are combinations of online and on-campus delivery systems. Even fully on-campus courses often use learning management systems and other technology to distribute content and foster collaboration and communication within the course. Self-directed and self-regulated learning models are used even in on-campus courses.

Previous studies found that among academic leaders, 64 percent believe that it takes more discipline for a learner to succeed in an online course (Sloan Consortium, 2006 & 2007), therefore, placing additional responsibility on students to be self-directed learners. Many of the students taking online, hybrid or technology rich courses are adult learners who are returning to college after several years with family and/or in the work force. Other students are traditional aged college students who have studied in primary and secondary schools with little or no technology integration or distance learning experience.

To what degree are these students ready to learn in a distance or technology rich environment? What types of support would be beneficial to help these students succeed at learning in a new paradigm?

The literature on human performance indicates that student success is typically a function of three factors – Aptitude, Attitude and Situation. Historically schools have done a great job collecting data about a student’s aptitude with prior GPA and standardized test scores. Most college admission decisions are made based on aptitude. However, college students, especially adult learners, often drop out because of factors related to their situation in life and/or their attitude toward the learning experience. It is these noncognitive indicators of student performance which are measured with the SmarterMeasure Learning Readiness Indicator. A predictive metric of student performance is not complete when based on just aptitude and demographic factors. A more complete picture of a learner’s propensity for success and persistence can be taken with the noncognitive variables which the assessment measures.

The purpose of the 2014 Student Readiness Report is to provide summary data from thousands of students at hundreds of colleges regarding their reported levels of readiness for studying online or in a technology rich environment. This data can inform educational leaders as they design and provide instruction and support students who are studying at a distance. The information in this report is aggregate data taken from the students’ scores on the SmarterMeasure Learning Readiness Indicator between the dates of June 1, 2013, and May 31, 2014. Data from secondary school students and trial accounts was not included in this report.

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The SmarterMeasure Learning Readiness Indicator, is a web-based assessment which measures a learner's readiness for succeeding in an online and/or technology rich learning program based on non-cognitive indicators of success. SmarterMeasure indicates the degree to which an individual student possesses attributes, skills and knowledge that contribute to success in learning. All seven components of SmarterMeasure are grounded in theoretical research and practice.

The purpose of the SmarterMeasure Learning Readiness Indicator is not to measure levels of academic skills. SmarterMeasure is designed to measure the levels of the non-cognitive traits, attributes and skills that learners possess that make distance learning or technology rich learning a good fit for them.

The seven components of SmarterMeasure are:

 Individual Attributes - motivation, procrastination, willingness to ask for help, etc.

 Life Factors - Availability of time, support from family and employers, finances, etc.

 Learning styles - Based on the multiple intelligences model

 Technical Competency - Skills with using technology

 Technical Knowledge - Knowledge of technology terms

 On-screen Reading Rate and Recall

 Keyboarding Speed and Accuracy

This is the sixth year that this report has been produced. In 2011 the title of the report was changed from the National Online Student Readiness Report to the Student Readiness Report to accommodate the fact that students in Canada and Puerto Rico are represented as well as students taking hybrid and/or

technology rich on-campus courses.

EXECUTIVE SUMMARY OF FINDINGS

Demographic Profile: In the twelve month period represented in this report, a total of 460,406

SmarterMeasure assessments were taken or provisioned. This is a 28% decrease from 639,324 from the prior year. This is largely due to the enrollments declining at one major, for-profit institution during this time period. For the purpose of this report, data from the K-12 students as well as all data from demonstration accounts was removed. Also for the purposes of this analysis only, records of persons who completed the entire exam were included. A total of 319,952 complete records were analyzed to determine descriptive statistics. A random sample of 2% (N= 6288) complete records was selected for the comparison of means across the demographic variables to control for the effects of a large sample. Data from 367 colleges and universities was analyzed for this report. This is a 33% increase from 275 schools from the prior year’s report.

The SmarterMeasure Learning Readiness Indicator was developed in 2002. The chart below illustrates assessment usage by year.

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Of the 460,406 SmarterMeasure testing instances from June 1, 2013 and May 31, 2014:

 67% were female (2013 = 69%, 2012 = 70%, 2011 = 71%, 2010 = 72%, 2009 = 66%)

 55% were Caucasian/White (2013 = 54%, 2012 = 59%, 2011 = 62%, 2010 = 59%, 2009 = 61%)

 The percentage of students who have never taken an online course is decreasing. (2014 = 51%,

2013 = 54%, 2012 = 54%, 2011 = 55%, 2010 = 60%, 2009 = 65%) This is an indicator that distance learning is becoming a more common educational delivery system.

 39% were traditional aged (24 and younger) college students (2013 = 40%, 2012 = 35%, 2011 =

30%, 2010 = 28%, 2009 = 32%)

 71% were students at an associate’s level institution (2013 = 53%, 2012 = 52%, 2011 = 45%,

2010 = 55%, 2009 = 67%)

 76% were from public institutions, and 24% were from private institutions. This figure excludes

the large set of data from one major for-profit institution.

 25% had “social” as their dominant learning style (2013 = 22%, 2012 = 22%, 2011 = 22%, 2010 =

28%, 2009 = 28%)

 46% scored within the 80% to 89% range on the individual attributes scale (2013 = 47%, 2012 =

45%, 2011 = 45%, 2010 = 45%, 2009 = 42%)

 26% recalled 90% or more of the reading passage (2013 = 28%, 2012 = 27%, 2011 = 27%, 2010

= 28%, 2009 = 28%) 0 100000 200000 300000 400000 500000 600000 700000 02 - 03 03 - 04 04 - 05 05 - 06 06 - 07 07 - 08 08 - 09 09 - 10 10 - 11 11 - 12 12 - 13 13 - 14

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 25% demonstrated 100% accuracy on the Keyboarding skills test (2013 = 25%, 2012 = 32%,

2011 = 30%, 2010 = 29%, 2009 = 27%) and typed an average of 26.24 words per minute (2012 = 27.73, 2011, 27.46, 2010 = 27.64, 2009 28.02)

 35% exhibited between 70% - 79% of mastery of technical knowledge (2013 = 36%, 2012 =

37%, 2011 = 37%, 2010 = 33%, 2009 = 33%)

 44% scored 100% on the technical competency skills tests (2013 = 47%, 2012 = 52%, 2011 =

53%, 2010 = 45%, 2009 = 58%)

 43% scored 80% - 89% on the life factors section (2013 = 43%, 2012 = 43%, 2011 = 44%)

Six-Year Trend Analysis

This is the sixth year that the Student Readiness Report has been produced. An analysis of the frequency data over the past five years yields some interesting observations about trends in distance learning.

 Females continue to take more distance learning courses than males.

 Ethnic diversity among distance learning students is remaining relatively constant.

 Fewer students are experiencing distance learning for the first time.

 Distribution of ages of distance learning students is remaining somewhat constant.

 Social continues to be the dominant learning style.

 Individual attributes scores are remaining constant.

 Reading recall scores are remaining constant.

 Keyboarding skills are remaining constant.

 Technical knowledge scores are remaining constant.

 Technical competency scores are remaining within a constant range.

2009 2010 2011 2012 2013 2014

Female

66%

72%

71%

70%

69%

67%

Caucasian/White

61%

59%

62%

59%

54%

55%

New to Online Learning

65%

60%

55%

54%

54%

51%

Traditional Age

32%

28%

30%

35%

40%

39%

Associates Level

67%

55%

45%

52%

53%

71%

Social as Dominant Learning Style

28%

28%

22%

22%

22%

25%

80% - 89% on Individual Attributes

42%

45%

45%

45%

47%

46%

Recalled 90% + on Reading

28%

28%

27%

27%

28%

26%

100% Accurate on Keyboarding

27%

29%

30%

32%

25%

25%

70% to 79% on Technical Knowledge 33%

33%

37%

37%

36%

35%

100% on Technical Competency

58%

45%

53%

52%

47%

44%

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A matrix was developed to illustrate the trends over a six year period of demographic factors that have had statistically significant higher means. A profile of a successful distance learning student is emerging with six demographic variables having a statistically significant higher mean for six years in a row on one or more constructs measured by SmarterMeasure.

 Females have had the highest means for six years in Individual Attributes.

 Males have had the highest means for six years in Technical Knowledge.



Caucasians have had the highest means for six years in Technical Knowledge.



Students who have taken five or more online courses have had the highest means for six years in

Individual Attributes and Technical Knowledge.

0% 10% 20% 30% 40% 50% 60% 70% 80% 2009 2010 2011 2012 2013 2014

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* 2

0

0

9

* 2

0

1

0

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2

0

1

1

*

2

0

1

2

*

2

0

1

3

* 2

0

1

4

Ind ivi d u al At tr ibu te s Readin g Re ca ll Readin g Rat e Keybo ar d ing Accur ac y Keybo ar d ing Rate T ec h n ica l Kn o w ledg e L if e Facto rs Academi c Att ribu tes Help S ee k ing P er sis ten ce P ro c ras tin atio n T ime Ma n agemen t L o cus of Co n tr o l Females

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**

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Males

_*

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*

*

*

**

*

*

Caucasian

_*

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*

*

*

*

*

***

*

*

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*

African American

_*

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AN, AI, PI

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Latino/Hispanic

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5 Prior Courses

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4 Prior Courses

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3 Prior Courses

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24 and younger

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25 - 34

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*

*

*

*

*

35 - 44

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43-47

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45 - 54

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55 and older

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*

*

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60+

Assoc. College

_*

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Bac. College

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Masters

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Special Focus Institution

**

*

*

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*

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Doct. Granting

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Private for-profit

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Private not-for-profit

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Public

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DESCRIPTION OF FINDINGS

Gender: Of the students taking SmarterMeasure during the 2013/2014 academic year and of those who reported gender, 67% were female and 33% were male.

Independent sample t-tests were calculated to determine if there are statistically significant differences between the means of gender and the constructs measured by SmarterMeasure. For scoring and reporting purposes each of the constructs measured by SmarterMeasure are quantified on a 0 to 100 scale. This scale is considered the composite score for that construct.

Females were found to have statistically significant higher means on the construct of individual attributes, Keyboarding rate and life factors. Males were found to have statistically significant higher means on the constructs of reading rate and technical knowledge. It should be noted that for many students a high reading rate could be interpreted as a negative attribute since it may indicate that the student skimmed the passage.

Ethnicity: The majority of students included in this report were Caucasian / White (55%). The second largest group was African American (24%).

Analysis of Variance (ANOVA) was calculated to determine if there are statistically significant differences between the means of the different ethnic groups and the constructs measured by SmarterMeasure. Statistically significant differences in means were reported in all constructs based on ethnicity. African-Americans reported the highest mean for Individual Attributes. Caucasian/White reported the highest mean for Reading Recall, Technical Knowledge, Technical Competency and Life Factors. Alaskan Native, American Indian or Pacific Islander reported the highest mean for Keyboarding Accuracy and Rate.

Number of Online Courses Taken: Institutions typically provide SmarterMeasure to students who have not yet taken an online or technology rich course. However, students who are new to one institution may have already taken an online course at another institution. This fact may impact their level of readiness to learn online. As a result, a demographic question is asked in SmarterMeasure to measure the number of online courses a student has already taken. The majority (51%) of students reported that they had never taken an online course prior to taking the SmarterMeasure assessment. The percentage of students who have never taken an online course is decreasing. (2014 = 51%, 2013 = 54%, 2012 = 54%, 2011 = 55%,

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2010 = 60%, 2009 = 65%) This is an indicator that distance learning is becoming a more common

educational delivery system.

Analysis of Variance (ANOVA) was calculated to determine the impact that a person taking prior online courses has on their readiness. The results demonstrated that experience matters with online learning. In each of the eight constructs measured, as persons took more online courses their readiness measures improved. The differences in the means were statistically significant in all of the seven scales. The greatest difference in means from students with no prior online course experience and those who had taken five or more courses continued (fourth consecutive year) to be in the area of technical knowledge. This indicates that with experience students can learn to use the technology required for online courses. Learners who had taken five or more prior online courses had statistically significant higher means for the constructs of Individual Attributes, Keyboarding Rate, Technical Knowledge, Technical Competency and Life Factors. Those who had taken two prior courses had the highest means for Keyboarding Accuracy. This paralleled the findings from the prior year.

Age Range: Learners are asked to report their age range. A large percentage (39%) of students taking SmarterMeasure during Academic Year 2013/2014 were college-age students (18 – 24). This is parallel to 40% the prior year. This indicates that the assessment is being used in more traditional collegiate programs, not solely in online programs which historically have been populated mostly by adult learners. Analysis of Variance (ANOVA) was calculated to determine if differences exist between age ranges. Significant differences did exist in six of the eight constructs measured. Generally speaking, age does matter as demonstrated below. For constructs related to personal maturity, older students had the highest means. For constructs related to technical matters, younger students had the highest means. This was consistent with the prior five years’ findings. As was stated earlier in this report, a higher mean for Reading Rate is not necessarily a good measure as it likely indicates that one is not spending enough time on the reading passage.

Institution Type: For analysis in this report, educational institutions using SmarterMeasure are classified by the type of institution. Available types include: (1) Doctorate-granting University, (2) Master’s College or University, (3) Baccalaureate College, (4) Associates College and (5) Other/Specialized. This report excluded data from special focus institutions, corporations and K12 institutions.

The majority (71%) of test takers were from Associates Colleges. Eight percent were from Master’s Colleges and Universities, 4% from Doctorate-granting Universities, 6% were from Baccalaureate Colleges and 11% from Other/Specialized. Seventy-six percent of data was from public institutions and 24% from private institutions. This distribution is parallel to the prior year.

Analysis of Variance (ANOVA) was calculated to determine if differences exist between students of different types of institutions. Significant differences did exist on six of the seven constructs measured. Master’s Colleges and Universities had the highest means for Individual Attributes, Life Factors,

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Recall and Technical Competency

Comparisons were also made between for-profit and not-for-profit institutions. Statistically significant differences in means existed in seven of the eight constructs measured. Public institutions had the highest mean for Life Factors and Keyboarding Accuracy. Private not-for-profit institutions had the highest means for Individual Attributes, Reading Recall, Keyboarding Rate, Technical Knowledge, and Technical Competency.

Dominant Learning Styles: Students learn using a variety of learning styles. Most persons are able to adapt their learning style to the format of the content. However, many persons do have a dominant learning style which defines their preferred method of receiving information. This analysis found that the most common dominant learning style was Social at 25%. (2013 = 22%, 2012 = 22%, 2011 = 22%, 2010 = 28%, 2009 = 28%) The least common dominant learning style was Visual (5%). The percentages of each learning style were within two percentage points of the prior year’s measurement. This is an indicator of the reliability of the learning styles instrument. This finding is of interest to instructional designers who seek to construct online courses which appeal across the learning styles.

Individual Attributes: On the SmarterMeasure assessment, students are asked several questions which quantify their levels of individual attributes. The following individual attributes are measured: (1) help seeking, (2) time management, (3) procrastination, (4) persistence, (5) academic attributes and (6) locus of control. These six individual attributes are reported in aggregate on a scale of 0 to 100 with 100 indicating a very high level of the attributes. Forty-six percent of students scored within the 80 – 89% range. The distribution of Individual Attributes scores is parallel to the findings of prior years. (80 – 89% range - 2014 – 46%, 2013 – 47%, 2012 = 45%, 2011 = 45%, 2010 = 45%, 2009 = 42%)

Reading Rate and Recall: On the SmarterMeasure assessment, students read a brief passage and then complete a quiz to measure the degree to which they can recall the information. The rationale is that much information in online courses is presented via text on-screen, and a person’s ability to remember what they have read is important. This report is encouraging in that 26% of students recalled 90% or more of what they read. Twenty-four percent recalled 80% – 89% of what they read. So over half of the students recalled 80% or more of what they had read. These findings replicated the findings from the prior year. The mean score was 71.76.

The average reading words per minute was 184 words per minute. The average words per minute for an American when reading for comprehension is 200 words per minute. Some students skim the reading passage, so reading rates in excess of 500 wpm were not factored into this average. However, one should not place too much stock in reading rate. Some students skim over the reading passage in SmarterMeasure and do not take the time to appropriately read it. As schools have conversations with students, they discuss the reality that when reading academic content, readers must not try to read too quickly.

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Keyboarding Speed and Accuracy: The average Keyboarding speed was 26.68 words per minute. (2013 = 26.24, 2012 = 27.73, 2011 = 27.46, 2010 = 27.64, 2009 = 28.02) This figure is the Adjusted Words per Minute and is adjusted to factor in the number of errors. However, the standard deviation of Adjusted Words per Minute was high at 14.107, so considerable variance was exhibited in Keyboarding skills among students who took SmarterMeasure during this academic year. Overall, students

demonstrated a high degree of accuracy when Keyboarding. Twenty-five percent of students

demonstrated 100% accuracy on the Keyboarding skills test. (2013 – 25%, 2012 – 32%, 2011 – 31%, 2010 – 29%) It is worthy of note that the average Keyboarding rate has been decreasing over the past six years. This may be an indication of weaker keyboarding skills.

Technical Knowledge: On the SmarterMeasure assessment, students are asked a series of questions which measure the degree to which they possess knowledge about technical terms and software usage. Technical Knowledge is measured on a scale of 0 – 100 with 100 being a high degree of Technical Knowledge. Thirty-five percent of students exhibited between 70% – 79% of mastery of Technical Knowledge. (2013 = 36%, 2012 = 37%, 2011 = 37%, 2010 = 33%, 2009 = 33%) The mean Technical Knowledge score was 72.43 with standard deviation of 12.581.

Technical Competency: Students are asked to complete a series of skill tests to demonstrate their level of competency with basic technical tasks. Overall, students performed well on this element of

SmarterMeasure with 44% scoring 100%. (2013 = 47%, 2012 = 54%, 2011 = 53%, 2010 = 45%, 2009 = 58%) The mean technical competency score was 91.58 with a standard deviation of 11.564.

Life Factors: On the SmarterMeasure assessment, students are asked a series of questions which measure several factors that are external to the learner. These factors include: availability of time, appropriateness of a place to study, one’s reason for taking online courses, resources available to the learner and academic skills. Forty-three percent scored in the 80% - 89% range. The mean Life Factors score was 79.88 with a standard deviation of 9.246.

BRIEF LITERATURE REVIEW ON LEARNER READINESS

With the shift toward online learning, it is important to explore the adoption of online education. Previous studies found that among academic leaders, 64 percent believe that it takes more discipline for a learner to succeed in an online course (Sloan Consortium, 2006); therefore, placing additional responsibility on students to be self-directed learners. Before the start of an online program or course, it should be

determined if a learner's instructional need can be resolved through a distance education approach (Willis & Lockee, 2004). Assessing the pre-requisite skills of the distance learner is critical (Hsiu-Mei & Liaw, 2004; Simonson et al., 2003). Learners need to have enough pre-requisite skills of technological

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proficiency and a strong motivation to learn by technology (Hsiu-Mei & Liaw, 2004). In a study by Kuh, (2005) of twenty highly engaged institutions, one common characteristic was to know the students— “where they came from, their preferred learning styles, their talents, and when and where they need help” (p. 301). Because of the difficulty in accommodating a group of learners with a wide range of acquired skills, requirements for pre-requisite skills should be set (Falvo & Solloway, 2004). A researched method of examining the notion of online readiness is listed using three aspects: (a) Student's preference for online form of instructional delivery as compared to traditional face to face instruction; (b) Student confidence in using electronic communication for learning and competence and confidence in the use of Internet and computer-mediated communication; and (c) Ability to engage in autonomous learning (P. J. Smith et al., 2003). Hall (2008, para 27) stated that "the primary value of the surveys may lie in raising awareness for any student considering enrolling in a distance education course."

Pamela Dupin-Bryant of Utah State University - Toole conducted a study which was published in The American Journal of Distance Education titled "Pre-entry Variables Related to Retention in Online Distance Education". This study identified pre-entry variables related to course completion and non-completion in university online distance education courses. Four hundred and sixty-four students who were enrolled in online distance education courses participated in the study. Discriminant analysis revealed six pre-entry variables were related to retention, including cumulative grade point average, class rank, number of previous courses completed online, searching the Internet training, operating systems and file management training, and Internet applications training. Results indicate prior educational experience and prior computer training may help distinguish between individuals who complete university online distance education courses and those who do not. SmarterMeasure measures all of the variables that this study indicated as indicators of success except for class rank.

Literature Review Resources

Dupin-Bryant, P. A. (2004). Pre-entry variables related to retention in online distance education. American Journal of Distance Education, 18(4), 199-206.

Falvo, D. A., & Solloway, S. (2004). Constructing community in a graduate course about teaching with technology. TechTrends: Linking Research & Practice to Improve Learning, 48(5), 56.

Hsiu-Mei, H., & Liaw, S.-S. (2004). Guiding distance educators in building web-based instructions. International Journal of Instructional Media, 31(2), 125.

Simonson, M., Smaldino, S., Albright, M., & Zvacek, S. (2003). Teaching and learning at a distance. Upper Saddle River, NJ: Pearson Education, Inc.

Willis, L. L., & Lockee, B. B. (2004). A pragmatic instructional design model for distance learning. International Journal of Instructional Media, 31(1), 9.

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2014 SUMMARY DATA AND ANALYSIS

Data were collected for this report from students who took the SmarterMeasure assessment from June 1, 2013, to May 31, 2014. In the twelve month period represented in this report, a total of 460,406

SmarterMeasure assessments were taken. For the purpose of this report, data from the K-12 students as well as all data from demonstration accounts was removed. A random sample of 2% (N = 6,288)

complete records was selected for the comparison of means across the demographic variables.

DEMOGRAPHIC FREQUENCIES

Note that schools have the option of not collecting demographic data from students. So the total number of demographic records analyzed does not equal the total number of assessments taken.

Gender: Of the students taking SmarterMeasure during the 2013/2014 academic year and of those who reported gender, 67% were female and 33% were male.

Gender Distribution

65467, 33%

130781, 67%

Male Female

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Ethnicity: The majority of students included in this report were Caucasian / White (55%). The second largest group was African American (24%).

Ethnicity

N

%

African-American

43768

24%

American Indian or Alaskan Native

1821

1%

Asian or Pacific Islander

8115

4%

Caucasian / White

102212 55%

Latino / Hispanic

24723

13%

Other Race

6054

3%

It should be noted that schools do have the option to opt out of asking this question to students, and if schools do ask the ethnicity question, it is not a required question.

43768, 24% 1821, 1% 8115, 4% 102212, 55% 24723, 13% 6054, 3% African-American

American Indian or Alaskan Native Asian or Pacific Islander

Caucasian / White Latino / Hispanic Other Race

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Number of Online Courses Taken: Institutions typically provide SmarterMeasure to students who have not yet taken an online or technology rich course. However, students who are new to one institution may have already taken an online course at another institution. This fact may impact their level of readiness to learn online. As a result, a demographic question is asked in SmarterMeasure to measure the number of online courses a student has already taken. The majority (51%) of students reported that they had never taken an online course prior to taking the SmarterMeasure assessment. The percentage of students who have never taken an online course is decreasing. (2014 = 51%, 2013 = 54%, 2012 = 54%, 2011 = 55%, 2010 = 60%, 2009 = 65%) This is an indicator that distance learning is becoming a more common educational delivery system.

Number of Prior Online Courses Taken

101588 25773 18922 13283 ₉₁₂₈ 31555 0 20000 40000 60000 80000 100000 120000 0 1 2 3 4 5+ 101588, 51% 25773, 13% 18922, 9% 13283, 7% 9128, 4% 31555, 16% 1 2 3 4 5 6

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Age Range: Learners are asked to report their age range. A large percentage (39%) of students taking SmarterMeasure during Academic Year 2013/2014 were college-age students (18 – 24). This is parallel to 40% the prior year. This indicates that the assessment is being used in more traditional collegiate programs, not solely in online programs which historically have been populated mostly by adult learners.

Distribution of Age Ranges

8678 78792 36450 24239 17992 12498 ₉₈₂₁ 6933 ₅₂₆₁ 1563 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 13-17 18-22 23-27 28-32 33-37 38-42 43-47 48-52 53-59 60+ 13-17 4% 18-22 39% 23-27 18% 28-32 12% 33-37 9% 38-42 6% 43-47 5% 48-52 3% 53-59 3% 60+ 1%

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Institution Type: For analysis in this report, educational institutions using SmarterMeasure are classified by the type of institution. Available types include: (1) Doctorate-granting University, (2) Master’s College or University, (3) Baccalaureate College, (4) Associates College and (5) Other/Specialized. This report excluded data from special focus institutions, corporations and K12 institutions.

The majority (71%) of test takers were from Associates Colleges. Eight percent were from Master’s Colleges and Universities, 4% from Doctorate-granting Universities, 6% were from Baccalaureate Colleges and 11% from Other/Specialized. Seventy-six percent of data was from public institutions and 24% from private institutions. This distribution is parallel to the prior year.

Distribution of Institution Type

Associates Colleges 71% Baccalaureate Colleges 6% Master's Colleges and Universities 8% Doctorate-granting Universities 4% Other / Specialized 11% Private for-profit 20% Private not-for-profit 4% Public 76%

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SECTION FREQUENCIES

Dominant Learning Styles: Students learn using a variety of learning styles. Most persons are able to adapt their learning style to the format of the content. However, many persons do have a dominant learning style which defines their preferred method of receiving information. This analysis found that the most common dominant learning style was Social at 25%. (2013 = 22%, 2012 = 22%, 2011 = 22%, 2010 = 28%, 2009 = 28%) The least common dominant learning style was Visual (5%). The percentages of each learning style were within two percentage points of the prior year’s measurement. This is an indicator of the reliability of the learning styles instrument. This finding is of interest to instructional designers who seek to construct online courses which appeal across the learning styles.

Distribution of Dominant Learning Styles

Social 25% Verbal 13% Logical 14% Aural 19% Solitary 16% Physical 8% Visual 5% 0 5000 10000 15000 20000 25000 30000 35000

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Individual Attributes: On the SmarterMeasure assessment, students are asked several questions which quantify their levels of individual attributes. The following individual attributes are measured: (1) help seeking, (2) time management, (3) procrastination, (4) persistence, (5) academic attributes and (6) locus of control. These six individual attributes are reported in aggregate on a scale of 0 to 100 with 100 indicating a very high level of the attributes. Forty-six percent of students scored within the 80 – 89% range. The distribution of Individual Attributes scores is parallel to the findings of prior years. (80 – 89% range - 2014 – 46%, 2013 – 47%, 2012 = 45%, 2011 = 45%, 2010 = 45%, 2009 = 42%)

Scores on the Individual Attributes Assessment

0 20000 40000 60000 80000 100000 120000 140000 30-39 40-49 50-59 60-69 70-79 80-89 90-100

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Reading Rate and Recall: On the SmarterMeasure assessment, students read a brief passage and then complete a quiz to measure the degree to which they can recall the information. The rationale is that much information in online courses is presented via text on-screen, and a person’s ability to remember what they have read is important. This report is encouraging in that 26% of students recalled 90% or more of what they read. Twenty-four percent recalled 80% – 89% of what they read. So over half of the students recalled 80% or more of what they had read. These findings replicated the findings from the prior year. The mean score was 71.76.

The average reading words per minute was 184 words per minute. The average words per minute for an American when reading for comprehension is 200 words per minute. Some students skim the reading passage, so reading rates in excess of 500 wpm were not factored into this average. However, one should not place too much stock in reading rate. Some students skim over the reading passage in SmarterMeasure and do not take the time to appropriately read it. As schools have conversations with students, they discuss the reality that when reading academic content, readers must not try to read too quickly.

Scores on the Reading Recall Assessment

0 10000 20000 30000 40000 50000 60000 70000 Less than 10% 10 -19% 20 -29% 30 -39% 40 -49% 50 -59% 60 -69% 70 -79% 80 -89% 90 -100%

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Keyboarding Speed and Accuracy: The average Keyboarding speed was 26.68 words per minute. (2013 = 26.24, 2012 = 27.73, 2011 = 27.46, 2010 = 27.64, 2009 = 28.02) This figure is the Adjusted Words per Minute and is adjusted to factor in the number of errors. However, the standard deviation of Adjusted Words per Minute was high at 14.107, so considerable variance was exhibited in Keyboarding skills among students who took SmarterMeasure during this academic year. Overall, students

demonstrated a high degree of accuracy when Keyboarding. Twenty-five percent of students

demonstrated 100% accuracy on the Keyboarding skills test. (2013 – 25%, 2012 – 32%, 2011 – 31%, 2010 – 29%) It is worthy of note that the average Keyboarding rate has been decreasing over the past six years. This may be an indication of weaker keyboarding skills.

Distribution of Keyboarding Speed and Accuracy

Average Words Per Minute by Academic Year

80% or less 4% 80% - 89% 11% 90% - 99% 60% 100% 25% 80% or less 80% - 89% 90% - 99% 100% 25 25.5 26 26.5 27 27.5 28 28.5 AY 08/09 AY 09/10 AY 10/11 AY 11/12 AY 12/13 AY 13/14

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Technical Knowledge: On the SmarterMeasure assessment, students are asked a series of questions which measure the degree to which they possess knowledge about technical terms and software usage. Technical Knowledge is measured on a scale of 0 – 100 with 100 being a high degree of Technical Knowledge. Thirty-five percent of students exhibited between 70% – 79% of mastery of Technical Knowledge. (2013 = 36%, 2012 = 37%, 2011 = 37%, 2010 = 33%, 2009 = 33%) The mean Technical Knowledge score was 72.43 with standard deviation of 12.581.

Scores on Technical Knowledge

0 20000 40000 60000 80000 100000 120000 Less than 10% 10 -19% 20 -29% 30 -39% 40 -49% 50 -59% 60 -69% 70 -79% 80 -89% 90 -100%

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Technical Competency: Students are asked to complete a series of skill tests to demonstrate their level of competency with basic technical tasks. Overall, students performed well on this element of

SmarterMeasure with 44% scoring 100%. (2013 = 47%, 2012 = 54%, 2011 = 53%, 2010 = 45%, 2009 = 58%) The mean technical competency score was 91.58 with a standard deviation of 11.564.

Scores on Technical Competency

0 20000 40000 60000 80000 100000 120000 140000 80% or less 80% - 89% 90% - 99% 100%

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Life Factors: On the SmarterMeasure assessment, students are asked a series of questions which measure several factors that are external to the learner. These factors include: availability of time, appropriateness of a place to study, one’s reason for taking online courses, resources available to the learner and academic skills. Forty-three percent scored in the 80% - 89% range. The mean Life Factors score was 79.88 with a standard deviation of 9.246.

Scores on Life Factors

0 20000 40000 60000 80000 100000 120000 140000 0 - 59% 60 - 69% 70 - 79% 80 - 89% 90 - 100%

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COMPARISON OF MEANS

Because this data set is so large (N=460,406), any comparison of means such as an independent sample t-test or an ANOVA will yield a statistically significant difference due to the magnitude of the sample size. To control for this impractical significance, a random sample of 2% (N=6288) of the records which had completed the full assessment was selected for analysis in this section. Random cases were selected using the random sample tool in SPSS (Statistical Program for Social Sciences).

It should be noted that when interpreting means for Reading Rate that a higher mean rate may not truly be indicative of faster readers. It could be indicative of readers who skimmed the passages instead of properly reading it. The average English-speaking adult reading speed when reading for comprehension is 200 – 250 words per minute. Mean reading speeds in excess of that should be evaluated accordingly. To test this assumption, discriminant analysis was computed to determine if one’s reading rate could be used to indicate their reading recall. Readers who likely skimmed the passaged were identified as those having reading rates of 400 wpm or higher. Using these two groups of normal and abnormally high readers, discriminant analysis was able to correctly classify their reading recall on a scale of 0 – 100 based on their reading rate 72.9% of the time. So more than 7 out of 10 times, readers who skim the passage also attain low scores on the reading recall section of SmarterMeasure.

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Gender: Independent sample t-tests were calculated to determine if there are statistically significant differences between the means of gender and the constructs measured by SmarterMeasure. For scoring and reporting purposes each of the constructs measured by SmarterMeasure are quantified on a 0 to 100 scale. This scale is considered the composite score for that construct.

Females were found to have statistically significant higher means on the construct of individual attributes, Keyboarding rate and life factors. Males were found to have statistically significant higher means on the constructs of reading rate and technical knowledge. It should be noted that for many students a high reading rate could be interpreted as a negative attribute since it may indicate that the student skimmed the passage.

Note: In the following tables the highest mean is underlined for ease of interpretation. Statistically significant differences in means are indicated in a bold, red font.

Group Statistics

Gender N Mean Std. Deviation Significance

Individual Attributes Male 1179 77.308210 7.9608470 .000

Female 2649 79.906391 7.2933411

Reading Recall Male 955 73.214743 18.6476069 .587

Female 2214 71.992380 18.5040000

Reading Rate Male 972 680.55 2287.053 .026

Female 2236 569.86 2106.105

Keyboarding Accuracy Male 944 91.73 17.201 .506

Female 2034 92.19 17.124

Keyboarding Rate Male 944 26.24 15.190 .001

Female 2034 27.48 12.259

Technical Knowledge Male 1108 73.308619 12.9950934 .004

Female 2418 72.136447 11.9780576

Technical Competency Male 1105 91.776896 11.5845980 .380

Female 2443 91.786181 11.3913434

Life Factors Male 1211 79.0300 9.39904 .006

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Ethnicity: Analysis of Variance (ANOVA) was calculated to determine if there are statistically significant differences between the means of the different ethnic groups and the constructs measured by

SmarterMeasure. Statistically significant differences in means were reported in all constructs based on ethnicity. African-Americans reported the highest mean for Individual Attributes. Caucasian/White reported the highest mean for Reading Recall, Technical Knowledge, Technical Competency and Life Factors. Alaskan Native, American Indian or Pacific Islander reported the highest mean for Keyboarding Accuracy and Rate.

N Mean

Std.

Deviation Significance

Individual Attributes African American 825 80.00 7.409 .000

Alaskan Native, American Indian or Pacific

Islander 184 77.05 7.899

Caucasian / White 1947 78.96 7.547

Latino / Hispanic 528 77.21 8.092

Other Race 119 77.48 7.667

Total 3603 78.80 7.677

Life Factors African American 835 79.93 9.768 .000

Alaskan Native, American Indian or Pacific

Islander 182 77.67 10.916

Caucasian / White 1960 80.19 8.423

Latino / Hispanic 514 77.96 9.014

Other Race 120 77.24 9.377

Total 3611 79.59 9.052

Reading Recall African American 747 66.45 20.619 .000

Alaskan Native, American Indian or Pacific

Islander 166 71.46 20.710

Caucasian / White 1664 75.27 17.851

Latino / Hispanic 464 69.61 19.693

Other Race 100 70.53 20.049

Total 3141 71.98 19.393

Reading Rate African American 747 819.07 2777.297 .038

Alaskan Native, American Indian or Pacific

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Caucasian / White 1664 563.84 2265.729 Latino / Hispanic 464 654.80 2810.171 Other Race 100 1174.94 3692.921 Total 3141 655.40 2507.761 Keyboarding Accuracy African American 659 91.15 16.145 .000

Alaskan Native, American Indian or Pacific

Islander 150 95.56 5.986

Caucasian / White 1619 93.95 13.408

Latino / Hispanic 389 92.50 15.462

Other Race 92 88.16 22.400

Total 2909 93.02 14.520

Keyboarding Rate African American 659 21.59 9.783 .000

Alaskan Native, American Indian or Pacific

Islander 150 30.67 13.158

Caucasian / White 1619 29.81 12.673

Latino / Hispanic 389 25.50 11.307

Other Race 92 23.73 12.457

Total 2909 27.23 12.414

Technical Knowledge African American 840 70.46 13.441 .000

Alaskan Native, American Indian or Pacific

Islander 187 73.95 13.509 Caucasian / White 1967 74.03 11.509 Latino / Hispanic 521 71.30 12.684 Other Race 118 73.20 12.282 Total 3633 72.78 12.375 Technical Competency African American 833 87.71 15.465 .000

Alaskan Native, American Indian or Pacific Islander 182 90.34 16.122 Caucasian / White 1921 93.24 9.746 Latino / Hispanic 517 89.90 13.204 Other Race 115 88.77 17.276 Total 3568 91.17 12.665

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Number of Online Courses Taken: Analysis of Variance (ANOVA) was calculated to determine the impact that a person taking prior online courses has on their readiness. The results demonstrated that experience matters with online learning. In each of the eight constructs measured, as persons took more online courses their readiness measures improved. The differences in the means were statistically significant in all of the seven scales. The greatest difference in means from students with no prior online course experience and those who had taken five or more courses continued (fourth consecutive year) to be in the area of technical knowledge. This indicates that with experience students can learn to use the technology required for online courses.

Learners who had taken five or more prior online courses had statistically significant higher means for the constructs of Individual Attributes, Keyboarding Rate, Technical Knowledge, Technical Competency and Life Factors. Those who had taken two prior courses had the highest means for Keyboarding Accuracy. This paralleled the findings from the prior year.

N Mean Std. Deviation Significance

Individual Attributes 0 1907 78.31 7.681 .000 1 491 78.49 7.751 2 370 78.75 7.250 3 248 79.47 8.001 4 162 80.46 7.232 5 606 80.85 7.113 Total 3784 78.95 7.620 Life Factors 0 1959 79.43 8.966 .000 1 497 79.63 9.089 2 386 79.44 9.062 3 250 78.89 9.863 4 166 79.91 8.876 5 620 81.28 8.490 Total 3878 79.74 8.996 Reading Recall 0 1624 70.61 19.850 .000 1 440 72.04 19.155 2 327 72.88 19.796 3 214 74.64 18.785 4 152 73.99 17.061

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5 521 76.36 17.252 Total 3278 72.36 19.273 Keyboarding Accuracy 0 1472 92.22 16.037 .001 1 417 92.35 16.828 2 305 95.49 5.630 3 202 91.68 19.442 4 139 92.96 13.206 5 487 94.73 11.086 Total 3022 92.97 14.896 Keyboarding Rate 0 1472 25.60 12.193 .000 1 417 27.26 12.207 2 305 29.87 12.220 3 202 27.75 12.351 4 139 27.52 12.069 5 487 30.83 12.589 Total 3022 27.33 12.423 Technical Knowledge 0 1979 70.63 12.902 .000 1 503 72.91 11.573 2 385 74.77 11.363 3 248 75.29 10.727 4 164 75.14 10.675 5 620 78.16 10.660 Total 3899 73.01 12.348 Technical Competency 0 1976 90.34 13.648 .000 1 495 92.11 10.452 2 378 91.33 12.256 3 238 92.67 11.162 4 162 92.58 9.600 5 582 94.17 9.303 Total 3831 91.49 12.313

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Age Range: Analysis of Variance (ANOVA) was calculated to determine if differences exist between age ranges. Significant differences did exist in six of the eight constructs measured. Generally speaking, age does matter as demonstrated below. For constructs related to personal maturity, older students had the highest means. For constructs related to technical matters, younger students had the highest means. This was consistent with the prior five years’ findings. As was stated earlier in this report, a higher mean for Reading Rate is not necessarily a good measure as it likely indicates that one is not spending enough time on the reading passage.

Age Range 20 14 Hig h es t Mean 20 13 Hig h es t Mean 20 12 Hig h es t Mean 20 11 Hig h es t Mean 20 10 Hig h es t Mean 20 09 Hig h es t Mean 24 and younger

Reading Rate Reading Rate

Keyboarding Accuracy Keyboarding Rate Keyboarding Rate Reading Rate Keyboarding Accuracy 25-34 Keyboarding Rate Keyboarding Accuracy Technical Competency Reading Recall Keyboarding Rate Technical Knowledge Technical Competency Life Factors Keyboarding Rate Reading Rate Technical Knowledge Learning Styles Reading Rate Keyboarding Rate Keyboarding Accuracy Technical Knowledge 35-44 Reading Recall Technical Knowledge Reading Recall Technical Competency Technical Knowledge Technical Knowledge 45-54 Individual Attributes 55 and older Individual Attributes Life Factors Individual Attributes Individual Attributes Life Factors Keyboarding Accuracy Individual Attributes Reading Recall Keyboarding Accuracy Life Factors Reading Recall Life Factors Individual Attributes Reading Recall

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N Mean Std. Deviation Significance

Individual Attributes 24 and younger 1656 76.77 7.797 .000

25 - 34 1154 79.79 7.403

35 - 44 585 80.68 7.135

45 - 54 306 81.39 6.225

55 and older 119 82.21 6.561

Total 3820 78.82 7.657

Life Factors 24 and younger 1688 79.48 9.130 .693

25 - 34 1187 79.53 9.211

35 - 44 600 79.68 8.818

45 - 54 316 80.03 8.688

55 and older 122 80.47 8.440

Total 3913 79.60 9.050

Reading Recall 24 and younger 1480 69.33 20.442 .000

25 - 34 998 73.64 19.036

35 - 44 499 75.07 17.286

45 - 54 254 74.54 17.695

55 and older 103 74.36 17.675

Total 3334 72.03 19.438

Keyboarding Accuracy 24 and younger 1359 93.34 13.660 .100

25 - 34 915 93.04 14.600

35 - 44 462 92.78 16.064

45 - 54 245 90.87 18.626

55 and older 102 90.82 18.479

Total 3083 92.89 14.942

Keyboarding Rate 24 and younger 1359 27.86 12.101 .000

25 - 34 915 27.96 12.846

35 - 44 462 26.25 11.995

45 - 54 245 23.95 12.048

55 and older 102 23.40 13.155

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Technical Knowledge 24 and younger 1706 70.64 12.098 .000

25 - 34 1190 75.08 11.903

35 - 44 601 75.31 12.181

45 - 54 316 73.54 13.171

55 and older 121 71.22 13.153

Total 3934 72.95 12.359

Technical Competency 24 and younger 1720 90.62 12.893 .002

25 - 34 1149 92.27 11.265

35 - 44 569 92.00 12.431

45 - 54 301 91.64 12.100

55 and older 119 89.43 15.655

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Institution Type: Analysis of Variance (ANOVA) was calculated to determine if differences exist between students of different types of institutions. Significant differences did exist on six of the seven constructs measured. Master’s Colleges and Universities had the highest means for Individual Attributes, Life Factors, Keyboarding Rate, and Technical Knowledge. Associates Colleges had the highest means for Reading Recall and Technical Competency

Comparisons were also made between for-profit and not-for-profit institutions. Statistically significant differences in means existed in seven of the eight constructs measured. Public institutions had the highest mean for Life Factors and Keyboarding Accuracy. Private not-for-profit institutions had the highest means for Individual Attributes, Reading Recall, Keyboarding Rate, Technical Knowledge, and Technical Competency.

For the purpose of this analysis data from corporations, special focus institutions and K12 institutions was excluded.

N Mean Std. Deviation Significance

Individual Attributes Associates College 2436 79.13 7.586 .000

Baccalaureate College 282 77.16 8.084

Master's Colleges and University 1752 79.90 7.545

Doctorate-Granting University 637 79.64 7.553

Total 5107 79.35 7.620

Life Factors Associates College 2560 80.28 8.904 .000

Baccalaureate College 334 78.02 9.110

Master's Colleges and University 1747 80.51 9.177

Doctorate-Granting University 639 77.98 9.250

Total 5280 79.94 9.096

Reading Recall Associates College 2089 72.36 18.922 .008

Baccalaureate College 312 68.33 20.123

Master's Colleges and University 1580 71.70 19.813

Doctorate-Granting University 540 71.50 19.161

Total 4521 71.75 19.369

Keyboarding Accuracy Associates College 2068 92.52 15.948 .099

Baccalaureate College 205 92.34 14.027

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Doctorate-Granting University 536 91.53 17.787

Total 4165 92.66 15.194

Keyboarding Rate Associates College 2068 26.67 12.341 .026

Baccalaureate College 205 24.51 10.889

Master's Colleges and University 1356 27.35 13.553

Doctorate-Granting University 536 26.80 13.561

Total 4165 26.80 12.852

Technical Knowledge Associates College 2470 72.37 12.105 .000

Baccalaureate College 293 72.38 13.515

Master's Colleges and University 1743 74.02 12.429

Doctorate-Granting University 643 71.37 13.012

Total 5149 72.81 12.445

Technical Competency Associates College 2591 91.93 11.024 .001

Baccalaureate College 336 89.28 14.637

Master's Colleges and University 1628 91.34 11.621

Doctorate-Granting University 559 91.03 11.885

Total 5114 91.47 11.594

Reading Rate Associates College 2089 597.46 2072.744 .759

Baccalaureate College 312 688.71 2787.400

Master's Colleges and University 1580 642.34 2453.706

Doctorate-Granting University 540 546.55 2061.650

Total 4521 613.36 2265.097

N Mean Std. Deviation Significance

Individual Attributes Private for-profit 739 80.18 7.127 .001

Private not-for-profit 206 80.42 6.744

Public 4015 79.18 7.761

Total 4960 79.38 7.639

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Private not-for-profit 206 79.43 8.966

Public 4141 80.21 9.072

Total 5133 80.04 9.090

Reading Recall Private for-profit 606 65.47 19.869 .000

Private not-for-profit 84 77.50 16.345

Public 3659 72.55 19.146

Total 4349 71.66 19.368

Keyboarding Accuracy Private for-profit 534 89.09 19.355 .000

Private not-for-profit 79 91.49 21.581

Public 3371 93.22 14.185

Total 3984 92.63 15.215

Keyboarding Rate Private for-profit 534 20.37 14.016 .000

Private not-for-profit 79 31.89 14.109

Public 3371 27.57 12.263

Total 3984 26.69 12.805

Technical Knowledge Private for-profit 795 70.29 14.183 .000

Private not-for-profit 206 78.64 10.613

Public 4009 73.08 12.083

Total 5010 72.87 12.481

Technical Competency Private for-profit 711 86.01 18.147 .000

Private not-for-profit 82 94.49 7.074

Public 4174 92.08 10.606

Total 4967 91.25 12.129

Reading Rate Private for-profit 606 589.74 1946.353 .688

Private not-for-profit 84 414.74 1378.089

Public 3659 620.04 2317.971

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ADDITIONAL ANALYSIS OF INDIVIDUAL ATTRIBUTES

The construct of individual attributes measured by the SmarterMeasure Learning Readiness Indicator contains the following factors: (1) help seeking, (2) time management, (3) procrastination, (4) persistence, (5) academic attributes and (6) locus of control. These factors are attributes of a person which can impact the degree to which they are comfortable and confident taking an online course.

“Help Seeking” is the degree to which a person is willing to ask for help when needed. “Time Management” is the degree to which a person can plan for the appropriate use of their time.

“Procrastination” is the degree to which a person completes tasks in a timely manner. “Persistence” is the degree to which a person maintains activity with a task until completion. “Academic Attributes” are indicative of a person’s prior academic success. “Locus of Control” is the degree to which a person feels that they are in control of their outcomes. Each of these factors was measured on a composite score ranging from 1 – 16 with 16 being a high degree of the attributes.

The bar charts below present a frequency report of the scores for each factor on the scale of 1 – 16 with 16 being a high degree of the desired measure of the attribute. These charts represent the frequencies of the random sample of 2% of the total data set.

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Procrastination Persistence

Academic Attributes Locus of Control

Additional analysis was conducted to determine if significant differences existed between the demographic groups and these individual attributes factors.

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Gender: Using an independent samples t-test, no significant differences were reported between the two genders across the individual attributes sub-scales.

N Mean Std. Deviation Significance

Academic Attributes Male 1099 13.31 2.599 .827

Female 2252 13.33 2.612

Total 3351 13.32 2.607

Help Seeking Male 1099 11.67 2.077 .425

Female 2252 11.60 2.115

Total 3351 11.62 2.102

Locus of Control Male 1099 10.78 2.712 .603

Female 2252 10.73 2.729 Total 3351 10.75 2.724 Persistence Male 1099 11.63 2.137 .910 Female 2252 11.62 2.158 Total 3351 11.63 2.150 Procrastination Male 1099 11.86 2.932 .423 Female 2252 11.95 2.909 Total 3351 11.92 2.916

Time Management Male 1099 13.79 2.604 .843

Female 2252 13.81 2.594

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Ethnicity: Significant differences were found between the ethnic groups in all six six individual attribute factors.

African Americans reported the highest means in each category except Locus of Control which was reported for Caucasian / White.

N Mean Std. Deviation Significance

Academic Attributes African American 825 14.06 1.750 .000

Alaskan Native, American Indian or Pacific Islander 184 13.58 1.940 Caucasian / White 1947 13.87 1.798 Latino / Hispanic 528 13.61 1.960 Other Race 119 13.82 1.779 Total 3603 13.86 1.824

Help Seeking African American 825 12.19 1.613 .000

Alaskan Native, American Indian or Pacific Islander 184 11.62 1.704 Caucasian / White 1947 12.04 1.559 Latino / Hispanic 528 11.72 1.598 Other Race 119 11.81 1.658 Total 3603 12.00 1.597

Locus of Control African American 825 10.97 2.142 .000

Alaskan Native, American Indian or Pacific Islander 184 10.66 1.996 Caucasian / White 1947 11.44 1.922 Latino / Hispanic 528 11.08 2.092 Other Race 119 10.91 2.063 Total 3603 11.22 2.022

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Persistence African American 825 12.45 1.687 .000

Alaskan Native, American Indian or Pacific Islander 184 12.15 1.724 Caucasian / White 1947 11.88 1.686 Latino / Hispanic 528 11.92 1.769 Other Race 119 11.90 1.773 Total 3603 12.03 1.719

Procrastination African American 825 12.78 2.439 .000

Alaskan Native, American Indian or Pacific Islander 184 12.02 2.559 Caucasian / White 1947 12.26 2.417 Latino / Hispanic 528 11.90 2.563 Other Race 119 11.84 2.511 Total 3603 12.30 2.471

Time Management African American 825 14.35 1.768 .000

Alaskan Native, American Indian or Pacific Islander 184 13.93 1.955 Caucasian / White 1947 14.31 1.817 Latino / Hispanic 528 13.90 1.936 Other Race 119 14.10 1.906 Total 3603 14.23 1.840

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Number of Prior Online Courses: Significant differences did exist depending on the number of prior online courses that a person has taken in five of the six categories of individual attributes. In each case the highest mean existed for persons who had taken five or more online courses with the exception of persistence and procrastination which was reported highest for persons having taken four prior online courses. This indicates that as a person’s experience with online courses increases, the degree to which their individual attributes are a good match for distance learning also increases.

N Mean Std. Deviation Significance

Academic Attributes 0 1907 13.78 1.809 .000 1 491 13.85 1.817 2 370 13.76 1.881 3 248 13.97 1.931 4 162 14.07 1.640 5 606 14.21 1.712 Total 3784 13.88 1.810 Help Seeking 0 1907 11.88 1.667 .000 1 491 11.92 1.586 2 370 12.06 1.452 3 248 12.08 1.589 4 162 12.23 1.454 5 606 12.42 1.402 Total 3784 12.01 1.594 Locus of Control 0 1907 11.13 1.967 .001 1 491 11.31 2.059 2 370 11.20 2.020 3 248 11.42 2.145 4 162 11.35 2.139 5 606 11.54 2.026 Total 3784 11.26 2.018 Persistence 0 1907 12.07 1.734 .133 1 491 11.95 1.752 2 370 11.92 1.672