2014 Student Readiness Report

Transcription

1 SmarterServices.com SMARTER

2 2 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 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 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, Data from secondary school students and trial accounts was not included in this report.

3 3 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 noncognitive 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 The chart below illustrates assessment usage by year.

4 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%)

5 5 25% demonstrated 100% accuracy on the Keyboarding skills test (2013 = 25%, 2012 = 32%, 2011 = 30%, 2010 = 29%, 2009 = 27%) and typed an average of words per minute (2012 = 27.73, 2011, 27.46, 2010 = 27.64, ) 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 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%

6 6 80% 70% 60% 50% 40% 30% 20% 10% % 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.

7 7 * 2009 * 2010 * 2011 * 2012 * 2013 * 2014 Individual Attributes Reading Recall Reading Rate Keyboarding Accuracy Keyboarding Rate Females ***** * ** * * ** * *** * * *** * Males **** * ***** * Caucasian *** ** * ***** ** * ** * * African American *** * * ** **** ** ** ** AN, AI, PI ** ** ** *** * * ** ** * * * * Latino/Hispanic * 5 Prior Courses ***** ** ** * ***** ** *** ** ** *** *** ** * * * * * * 4 Prior Courses * * * * * 3 Prior Courses * 24 and younger * * * * *** ** * ** *** * * * * * * * * * 55 and older ***** ** *** ** * 60+ Assoc. College * * * Bac. College ** * ** * *** ** Masters * * * * ** ** * ** ** * Special Focus ** * * * * ** Institution Doct. Granting * ** Private for-profit * Technical Knowledge Life Factors Academic Attributes Help Seeking Persistence **** ** Procrastination *** * Time Management *** * Locus of Control ** *

8 8 Private not-for-profit ** ** * ** * *** ** * * * * ** * Public * * ** ** * 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%,

9 = 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, Keyboarding Rate, and Technical Knowledge. Associates Colleges had the highest means for Reading

10 10 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 %, %, 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 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.

11 11 Keyboarding Speed and Accuracy: The average Keyboarding speed was 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 , 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. ( %, %, %, %) 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 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 with standard deviation of 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 with a standard deviation of 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 with a standard deviation of 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

12 12 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 noncompletion 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), 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.

13 SUMMARY DATA AND ANALYSIS Data were collected for this report from students who took the SmarterMeasure assessment from June 1, 2013, to May 31, 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% Male Female , 67%

14 14 Ethnicity: The majority of students included in this report were Caucasian / White (55%). The second largest group was African American (24%). 6054, 3% 24723, 13% 43768, 24% African-American 8115, 4% 1821, 1% American Indian or Alaskan Native Asian or Pacific Islander Caucasian / White Latino / Hispanic , 55% Other Race Ethnicity N % African-American % American Indian or Alaskan Native % Asian or Pacific Islander % Caucasian / White % Latino / Hispanic % Other Race % 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.

15 15 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 , 4% 13283, 7% 18922, 9% 31555, 16% 25773, 13% , 51%

16 16 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 % % % 60+ 1% % % % % % %

17 17 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 Other / Specialized 11% Master's Colleges and Universities 8% Doctorategranting Universities 4% Baccalaureate Colleges 6% Associates Colleges 71% Private forprofit 20% Private notfor-profit 4% Public 76%

18 18 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 Physical 8% Visual 5% Solitary 16% Aural 19% Logical 14% Social 25% Verbal 13% Social Verbal Logical Aural Solitary Physical Visual

19 19 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 %, %, 2012 = 45%, 2011 = 45%, 2010 = 45%, 2009 = 42%) Scores on the Individual Attributes Assessment

20 20 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 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 Less than 10% 10-19% 20-29% 30-39% 40-49% 50-59% 60-69% 70-79% 80-89% %

21 21 Keyboarding Speed and Accuracy: The average Keyboarding speed was 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 , 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. ( %, %, %, %) 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 80% or less 4% 100% 25% 80% - 89% 11% 90% - 99% 60% 80% or less 80% - 89% 90% - 99% 100% Average Words Per Minute by Academic Year AY 08/09 AY 09/10 AY 10/11 AY 11/12 AY 12/13 AY 13/14

22 22 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 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 with standard deviation of Scores on Technical Knowledge Less than 10% 10-19% 20-29% 30-39% 40-49% 50-59% 60-69% 70-79% 80-89% %

23 23 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 with a standard deviation of Scores on Technical Competency % or less 80% - 89% 90% - 99% 100%

24 24 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 with a standard deviation of Scores on Life Factors % 60-69% 70-79% 80-89% %

25 25 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 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 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.

26 26 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 Female Reading Recall Male Female Reading Rate Male Female Keyboarding Accuracy Male Female Keyboarding Rate Male Female Technical Knowledge Male Female Technical Competency Male Female Life Factors Male Female

27 27 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. Std. N Mean Deviation Significance Individual Attributes African American Alaskan Native, American Indian or Pacific Islander Caucasian / White Latino / Hispanic Other Race Total Life Factors African American Alaskan Native, American Indian or Pacific Islander Caucasian / White Latino / Hispanic Other Race Total Reading Recall African American Alaskan Native, American Indian or Pacific Islander Caucasian / White Latino / Hispanic Other Race Total Reading Rate African American Alaskan Native, American Indian or Pacific Islander

28 28 Caucasian / White Latino / Hispanic Other Race Total Keyboarding African American Accuracy Alaskan Native, American Indian or Pacific Islander Caucasian / White Latino / Hispanic Other Race Total Keyboarding Rate African American Alaskan Native, American Indian or Pacific Islander Caucasian / White Latino / Hispanic Other Race Total Technical Knowledge African American Alaskan Native, American Indian or Pacific Islander Caucasian / White Latino / Hispanic Other Race Total Technical African American Competency Alaskan Native, American Indian or Pacific Islander Caucasian / White Latino / Hispanic Other Race Total

29 29 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 Total Life Factors Total Reading Recall

30 Total Keyboarding Accuracy Total Keyboarding Rate Total Technical Knowledge Total Technical Competency Total

31 31 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 2014 Highest Mean 2013 Highest Mean 2012 Highest Mean 2011 Highest Mean 2010 Highest Mean 2009 Highest Mean 24 and younger Reading Rate Keyboarding Rate Keyboarding Accuracy Technical Competency Reading Recall Technical Knowledge Reading Rate Keyboarding Accuracy Reading Recall Keyboarding Rate Technical Knowledge Technical Competency Life Factors Keyboarding Rate Reading Rate Technical Knowledge Reading Recall Technical Competency Keyboarding Rate Learning Styles Reading Rate Technical Knowledge Keyboarding Rate Reading Rate Keyboarding Accuracy Technical Knowledge Individual Attributes 55 and Individual older Attributes Individual Attributes Life Factors Individual Attributes Life Factors Keyboarding Accuracy Individual Attributes Reading Recall Keyboarding Accuracy Life Factors Reading Recall Life Factors Keyboarding Rate Keyboarding Accuracy Technical Knowledge Individual Attributes Reading Recall

32 32 N Mean Std. Deviation Significance Individual Attributes 24 and younger and older Total Life Factors 24 and younger and older Total Reading Recall 24 and younger and older Total Keyboarding Accuracy 24 and younger and older Total Keyboarding Rate 24 and younger and older Total

33 33 Technical Knowledge 24 and younger and older Total Technical Competency 24 and younger and older Total

34 34 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 Baccalaureate College Master's Colleges and University Doctorate-Granting University Total Life Factors Associates College Baccalaureate College Master's Colleges and University Doctorate-Granting University Total Reading Recall Associates College Baccalaureate College Master's Colleges and University Doctorate-Granting University Total Keyboarding Accuracy Associates College Baccalaureate College Master's Colleges and University

35 35 Doctorate-Granting University Total Keyboarding Rate Associates College Baccalaureate College Master's Colleges and University Doctorate-Granting University Total Technical Knowledge Associates College Baccalaureate College Master's Colleges and University Doctorate-Granting University Total Technical Competency Associates College Baccalaureate College Master's Colleges and University Doctorate-Granting University Total Reading Rate Associates College Baccalaureate College Master's Colleges and University Doctorate-Granting University Total N Mean Std. Deviation Significance Individual Attributes Private for-profit Private not-for-profit Public Total Life Factors Private for-profit

36 36 Private not-for-profit Public Total Reading Recall Private for-profit Private not-for-profit Public Total Keyboarding Accuracy Private for-profit Private not-for-profit Public Total Keyboarding Rate Private for-profit Private not-for-profit Public Total Technical Knowledge Private for-profit Private not-for-profit Public Total Technical Competency Private for-profit Private not-for-profit Public Total Reading Rate Private for-profit Private not-for-profit Public Total

37 37 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. Help Seeking Time Management

38 38 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.