Research

Research Results from Individual Schools

View Compilation of Research Findings From Several Schools

MIDDLESEX COMMUNITY COLLEGE - Middletown, Connecticut

To answer whether SmarterMeasure scores affect students' grades in online learning, a correlation study was conducted to see the relationships between the scores of SmarterMeasure and the students' grades. The preliminary study done in Spring 2009 and Summer 2009 on 750 cases showed a significant correlation between the score of personal attributes and grades. They were significantly correlated with a positive coefficient, meaning that the higher a score of personal attributes, the higher grade a student would receive. This result implies that personal attributes, represented by self-motivation, self-discipline, and time management, plays a very important role in student success of online learning. This preliminary study was followed by a subsequent study Fall 2010 which analyzed grades on 3228 cases collected across six academic terms. The result confirmed a significant correlation between the score of personal attributes and students' grades. Middlesex Community College used these findings to modify the types of student services that they provide to online learners. This pattern of learner readiness assessment coupled with providing appropriate services to match their deficiencies resulted in substantial gains in student retention. Before SmarterMeasure was implemented, 6% to 13% more students failed online courses than students taking on-ground courses. After the implementation, the gaps were narrowed; 1.3% to 5.8% more online students failed than on-ground students.

J. SARGEANT REYNOLDS COMMUNITY COLLEGE - Richmond, Virginia

As part of its Quality Enhancement Plan (QEP), J. Sargeant Reynolds Community College adopted SmarterMeasure, an assessment tool that assesses student readiness for learning within the online classroom. An analysis was conducted to determine the relationship between the SmarterMeasure sub-scale scores and student's grades. Among the results, the top factors that demonstrate the highest correlation between SmarterMeasure performance and students' academic success are the following:

  • Skills - The results indicated that 66% of the students who scored Medium-High to High in the Skills factor succeeded in their online classes. By contrast, only 5% of students who scored Low-Medium in the Skills section were successful.
  • Time - Of those who scored Medium-High to High by demonstrating that they had an adequate resource of time, 62% were academically successful; only 10% of those who scored Low-Medium to Low were similarly successful.
  • Resources - The results indicated that 66% of the students who scored Medium-High to High in the Resources factor succeeded in their online classes, and only 5% of students who scored Low or Low-Medium in the Resources section were successful.
  • Place - Among those who scored Medium-High to High, 72% were successful in their online courses

NORTH CENTRAL MICHIGAN COLLEGE - Petoskey, MI

Leaders at North Central Michigan College recognize the value of multiple different assessments of students in the admissions process. In addition to using SmarterMeasure to measure levels of online student readiness, they also use the COMPASS exam (provided by ACT) to measure incoming student's skills in reading, writing and math. To determine the degree of relationship between measures of online learner readiness and measures of academic readiness they computed correlations between the scores for the two exams. Statistically significant correlations were found between four of the six SmarterMeasure scales and sections of the Compass exam.

  COMPASS
SMARTERMEASURE Math English Reading E-Write
Learning Styles   X X X
Reading X X X X
Individual Attributes     X  
Life Factors     X X

X = Statistically Significant Correlation ( p < 0.05)

The providers of SmarterMeasure encourage schools to do research with SmarterMeasure data regarding their own students. When schools plan to do an analysis of their SmarterMeasure data they often plan first to correlate SmarterMeasure scores to student's grades in the course. This is a welcomed analysis and typically results in statistically significant findings. The 2008 study conducted by Atanda Research analyzed the SmarterMeasure scores of 2,622 random students representing over 300 schools. Correlations significant at the .05 level or higher were found with 11 of the 15 SmarterMeasure scores variables and student's grades. However, this analysis is really not the most appropriate way to measure the construct validity of SmarterMeasure scores because student's grades are impacted by a myriad of variables (prior academic experiences, IQ, etc.). SmarterMeasure is not designed to be an indicator of academic success. There are several tools such as the ACT, SAT, and GRE which serve this purpose. SmarterMeasure does not measure any constructs of content knowledge in areas such as math, science, history, etc. So to use SmarterMeasure solely as a predictor of academic success is not the most appropriate application.

In addition to correlating SmarterMeasure scores to grades, we recommend three other types of analysis which may be a more valid measurement of the applicability of SmarterMeasure. (1) Identify students who dropped out of the courses and compare the means of their SmarterMeasure scores to the means of the SmarterMeasure scores of the students who persisted in the courses. The intent of SmarterMeasure is to identify students who are "at-risk" of not being a good fit for distance or technology rich learning and it is these students who are more likely to drop out. The real benefit of SmarterMeasure is when schools can identify "at-risk" students then provide the encouragement, remediation and support that the students need to remain in the course and be successful. (2) After students have completed their first online, hybrid or technology rich course, do a survey of the students asking them to report the goodness of fit for them. Ask them questions about how they did keeping up with the volume of reading in the course, the degree to which they could find time to participate in course activities, the level of frustration they had with their computer and the Internet, etc. Then correlate their responses on these questions back to their SmarterMeasure scores. This type is study is very appropriate because SmarterMeasure is intended to be a predictor of goodness of fit of distance or technology rich education. In the 2008 study conducted by Atanda Research, of the 90 correlations calculated between measures of goodness of fit and SmarterMeasure scores 63 of the 90 correlations were statistically significant at the .05 level or higher. (3) The third type of analysis that we encourage is a qualitative study in which you interview individually or in a focus group students who persisted in online or technology rich courses and those who withdrew. Compare the factors that influenced their decision to remain or withdraw to the means of the SmarterMeasure scores from your students.

To assist schools in planning a research project using their SmarterMeasure data we provide a Research Plan document. This document describes various research methodologies that schools have used to analyze their SmarterMeasure data. Not only does the provider of SmarterMeasure support additional analysis of SmarterMeasure data, but we will support you in the effort. If your school would like to construct a study like this contact Dr. Mac Adkins for assistance in designing the study, exporting the correct data, and conducting the statistical analysis.



2011 Student Readiness Report

SmarterServices, LLC, the provider of the SmarterMeasure Learning Readiness Indicator, annually analyzes the SmarterMeasure data in aggregate of all of the students from the prior year who have taken SmarterMeasure. No data specific to individual students or individual schools is made publicly available. Data in the 2011 report was taken from 240,386 unique students from 258 higher education institutions who took the SmarterMeasure assessment from July 1, 2010 to June 30, 2011. Highlights in the report include the following statistically significant differences between the means of the variables of gender, ethnicity, institution type, age range, and number of prior online courses taken as they relate to student readiness for online learning:

  • Gender: Females were found to have statistically significant higher means on the constructs of individual attributes, typing accuracy, and life factors. Males were found to have statistically significant higher means on the constructs of reading rate and technical knowledge.
  • Ethnicity: Statistically significant differences in means were reported in seven of the eight constructs based on ethnicity. Caucasian/White reported the highest means for individual attributes, reading recall, typing accuracy, and technical knowledge. Asian or Pacific Islander reported the highest mean for reading rate and typing rate.
  • Age Range: Significant differences did exist between the age categories on the factors of individual attributes. In this analysis, it is clear that one's individual attributes in relation to online learning do improve with age. 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 two years' findings.
  • Number of Courses: The results demonstrated that experience matters with online learning. In six of eight constructs measured, persons who reported having taken five or more prior online courses reported the highest mean. The differences in the means were statistically significant in five of the eight groups. The greatest difference in means from students with no prior online course experience and those who had taken five or more courses continued to be in the area of technical knowledge. This indicates that with experience students can learn to use the technology required for online courses.
  • 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 eight constructs measured. Baccalaureate institutions had a statistically significant higher mean in the constructs of life factors and individual attributes while Special Focus Institutions had the highest means for reading recall and typing accuracy. Corporations had the highest means for learning styles and technical knowledge.

This is the third year that the Student Readiness Report has been produced. For three years in a row females have had statistically significant higher means in Individual Attributes, Academic Attributes, and Time Management. Males have had statistically significant higher means for Technical Knowledge for three years. African Americans have had significantly higher means for three years in Help Seeking. Students who have taken five or more online courses reported statistically higher means for the three years in Individual Attributes, Technical Knowledge, and Procrastination.

A full copy of the report is available here.





Assessment Details

All seven components of SmarterMeasure are grounded in theoretical research and practice. The seven components of SmarterMeasure are:

The SmarterMeasure Learning Readiness Indicator is composed of multiple scales with each section measuring multiple sub-scales. Schools have the ability to opt-out of administering any scale to their students. Schools may also re-order the sequence through which the scales are delivered to the students.

Section of SmarterMeasure Sub-scales Number of Items
Life Factors
  • Availability of time to study
  • Availability of a dedicated place to study
  • Reason for continuing one's education
  • Support resources from family, friends and employers
  • Perception of academic skills
 20
Individual Attributes
  • Procrastination
  • Time management
  • Persistence
  • Willingness to ask for help
  • Academic attributes
  • Locus of control
 24
Learning Styles Identifies the degree to which they posses each of the following learning styles:
  • Visual
  • Verbal
  • Social
  • Solitary
  • Physical
  • Aural
  • Logical
 21 or 35
Reading Skills
  • Reading Rate
  • On-screen reading recall
 11
Technical Knowledge
  • Technology usage
  • Technology in your life
  • Technology vobacbulary
  • Personal computer/Internet specifications
 23
Technical Competency
  • Computer competency
  • Internet competency
 10
Typing Skills
  • Typing rate
  • Typing accuracy
 1
OPTIONAL SECTIONS SCHEDULED FOR FUTURE RELEASE
Math Readiness
  • Numbers
  • Algebra
  • Geometry
  • Modeling
  • Technology
 30
Writing Readiness
  • Writing apprehension
  • Grammar, usage and style
  • Structure of academic writing
 30
Faculty Version
  • Questions and feedback re-structured to be applicable to faculty.
 Variable
NOTE: There are multiple questions on the assessment to measure each of the sub-scales.



Construct Validity

Construct validity refers to whether an assessment measures a theorized psychological construct. In the case of SmarterMeasure, construct validity is a measurement of the degree to which SmarterMeasure is an indicator of a learner's level of readiness for studying in an online or technology rich environment. Results from the three studies described below indicate that SmarterMeasure has strong construct validity in that it is an indicator of the goodness of fit for distance learning as is evidenced by multiple correlations that are statistically significant at the .01 level.

It should be noted that SmarterMeasure is not designed to be a predictor of academic success. There are a myriad of variables which impact academic success in online courses ranging from the student's intelligence to the level of interactivity of the online faculty member. SmarterMeasure is an indicator of the degree to which online, hybrid and technology rich courses are a good fit for a student. SmarterMeasure does not make a value judgment indicating that a student should or should not take the courses. Rather it informs the student of their strengths and opportunities for growth in areas related to taking these type courses. If a student is indicated to be deficient in a certain area and then if the school provides appropriate remediation and/or support, then SmarterMeasure can serve as a retention tool by helping students succeed as they learn in the context of online or technology rich courses.

In 2011 a major for-profit university conducted an extensive validity study to determine if SmarterMeasure was being an accurate indicator of the student success variables of academic achievement, engagement, satisfaction and retention. Statistically significant relationships were found between SmarterMeasure scores and each of these four constructs. A summary of these findings is provided below you can read a copy of the final report of Phase One and Phase Two of this study.

Academic Achievement and Retention were compared to SmarterMeasure scores using grade and enrollment data.

  • The measures of Individual Attributes, Technical Knowledge, and Life Factors had statistically significant mean differences with the measures of GPA.
  • The measure of Learning Styles had a statistically significant mean difference between students who were retained and those who left. A 73% classification accuracy of this retention measure was achieved.
  • The measures of Individual Attributes and Technical Knowledge were statistically significant predictors of retention as measured by the number of courses taken per term.

Satisfaction and Engagement were compared to SmarterMeasure scores using students' responses to an online survey.

  • The measures of Individual Attributes and Life Factors had statistically significant mean differences on six of the seven survey items. Reading Rate, Technical Knowledge, and Technical Competency had significant differences on four of the seven items.
  • The measures of Individual Attributes and Technical Competency had statistically significant relationships with the four survey items related to Engagement. The items of hours per week spent on course related activities; number of times per week logging into course; length of discussion board postings; and number of times contacting technical support can be predicted given knowledge of Individual Attributes, and more specifically the subscales listed.
  • The measures of Life Factors, Individual Attributes, Technical Competency, Technical Knowledge, and Learning Styles were used to correctly classify responses to the survey questions related to engagement and satisfaction with up to 93% classification accuracy.
  • Structural equation modeling was used to create a hypothesized theoretical model to determine if SmarterMeasure scores would predict satisfaction as measured by the survey. Results indicated that prior to taking online courses, student responses to the readiness variables were important indicators of later student satisfaction/retention. The structural coefficient for Ready predicting Satisfy, = .36, was statistically significant (z = 6.01, p = .0001). Therefore, the multiple SmarterMeasure assessment scores are a statistically significant positive predictor of the survey responses.

Further analysis revealed that the predictive nature of SmarterMeasure scores as classified by the Readiness Ranges can be improved using recommended adjustments to the grading thresholds.

The majority of survey participants (90%) either somewhat or definitely remembered taking the assessment. The majority of survey participants (89%) found the assessment somewhat useful, useful, or very useful; while only 11% did not find it useful at all as a student service.

Phase two of the study drilled down into the data at the sub-scale level Statistically significant relationship were found between SmarterMeasure data and student success categories related to academic success and retention. The table below indicates which sub-scales had statistically significant relationships with these key performance indicators.

SmarterMeasure Scale Readiness Domain Subscales
  RETENTION
Postive vs. Negitive		 ACADEMIC SUCCESS
Pass Vs. Fail
Life Factors Place, Reason, and Skills Place
Learning Styles Social and Logical N/A
Personal Attributes Academic, Help Seeking, Procrastination, Time Management, and Locus of Control Time Management
Technical Competency Internet Competency Internet Competency and Computer Competency
Technical Knowledge Technology Usage and Technical Vocabulary Technical Vocabulary

A predictive model using multiple regression was created to measure the degree to which SmarterMeasure sub-scales are predictors of academic success as measured by GPA. Each set of subscales for the Readiness Domains were considered a theoretical set of independent predictor variables, therefore separate regression analyses were conducted on each. The table below illustrates that GPA was significantly predicted by Place, Skills, Verbal, Logical, Help Seeking, Time Management, Locus of Control, Computer Competency, Internet Competency, and Technology Vocabulary.

Readiness Domain GPA F P
Life Factors Place and Skills 12.35 .0001
Learning Styles Verbal and Logical 3.95 .02
Personal Attributes Help Seeking, Time Management, and Locus of Control 22.11 .0001
Technical Competency Computer and Internet Competency 22.75 .0001
Technical Knowledge Technology Vocabulary 38.76 .0001

In 2007 an external research firm (Atanda Research, Alexandria, VA) was commissioned to analyze the data gathered during a study concerning the relationship of SmarterMeasure scores and measures of academic success and goodness of fit of distance education as a measure of construct validity. The major findings of this report were that there were forty-two statistically significant correlations between SmarterMeasure variables and measures of academic success and goodness of fit. Of the five constructs measured by SmarterMeasure, the construct with the most correlation to academic success and goodness of fit was Individual Attributes. The variable of the participant's individual attributes scores were statistically significant at the .001 level with all measures of academic success and goodness of fit. The variable with the strongest correlation in the study was relationship between Grade Point Average and Reading Comprehension. Click here to view a copy of this report.

In 2008 the study conducted by Atanda Research was replicated as a part of a learner's dissertation research which involved 2,622 students who had taken SmarterMeasure representing over 300 schools. This replication yielded even stronger results than the original study. Of the possible 105 correlations measured, 74 were found to be statistically significant. The factor measured by SmarterMeasure that had the strongest correlations to measures of goodness of fit and academic success was individual attributes which yielded correlations in each of the seven categories which were statistically significant at the .01 level. This finding mirrored the finding from the 2007 study which also indicated that individual attributes were the strongest indicator of goodness of fit of distance education.

The following correlation matrix presents the results of the statistical analysis from this study:

SmarterMeasure Scores Measures of Goodness of Fit Measure of Academic Success
  Reading Required Find Time Computer Skills Internet Access Good Choice Take Another GPA
Individual Attributes .200** .203** .147** .147** .228** .176** .218**
Overall Tech Competency .013 -.014 .170** .154** .114** .109** .144**
Computer Competency .011 -.016 .089** .079** .065** .068** .095**
Internet Competency .007 -.009 .162** .146** .108** .098** .119**
Tech. Knowledge .080** .04 .307** .242** .200** .173** .149**
Reading Comprehension -.007 -.052 .128** .101** .074** .083** .194**
Typing W.P.M. .043* .04 .236** .210** .159** .167** .188**
Typing Accuracy .059** .025 .083** .073** .055** .056** .093**
Visual Learning Style 0 -.007 .041* .008 .013 -.012 .014
Social Learning Style .082** .061** .095** .067** .047* .039 .003
Physical Learning Style -.007 .005 -.003 .001 -.004 -.016 -.038
Aural Learning Style .037 .04 .103** .081** .033 .022 -.011
Verbal Learning Style .162** .101** .143** .119** .131** .102** .073**
Solitary Learning Style .091** .072** .089** .076** .085** .074** .067**
Logical Learning Style .115** .079** .157** .144** .126** .108** .071**

*  Correlation is significant at the .05 level
** Correlation is significant at the 0.01 level



Item Reliability

In statistics, reliability is the consistency of a set of measurements used in an assessment. It is a measurement of whether the items of an instrument give or are likely to give the same measurement upon multiple attempts.

In 2011 Applied Measurement Associates of Tuscaloosa, Alabama was commissioned to conduct reliability coefficient calculations for the questions\items in SmarterMeasure. An expected range for Cronbach Alpha reliability coefficient values is expected to be from .70 to .95 to indicate a reliable assessment.

Scale Cronbach Alpha Reliability Scale Type Number of Items Sample Size
Learning Styles .81 0,1,2 21 873
Learning Styles .81 0,1,2 35 28,056
Individual Attributes .80 1,2,3,4 24 29,989
Life Factors .76 1,2,3,4 20 30,004
Technical Knowledge .75 0,1 23 29,992
Technical Competency .38 0,1 10 30,001

A Cronbach Alpha Reliability Coefficient of .80 indicates that 80% of the score can be consistently reproduced using the assessment items.

It should be noted that for the areas of SmarterMeasure which showed a lower reliability coefficient that the scale type was 0,1. This scale type resulted in lower levels of variability among the possible answers thus reducing the measurement of reliability.

One of the useful features of SmarterMeasure is that school leaders (faculty and/or administrators) can view SmarterMeasure scores through a dashboard which allows them to at-a-glance identify students who might be at risk of not doing well in an online or technology rich course based on their SmarterMeasure scores. Then based on these findings the school can provide remediation and support as appropriate. This serves as a valuable student service which can increase the retention rates among online learners. Because the student population of each school is unique, one of the features of SmarterMeasure is that schools can set the grading thresholds to determine what level of SmarterMeasure scores should classify their students as "failed","questionable", or "passed". In July, 2008 an analysis was conducted based on the 108,423 students who had taken SmarterMeasure in the prior twelve months. Based on this analysis recommendations were made regarding the setting of the grading threshold values in the administrative dashboard of SmarterMeasure. Click Here to view a copy of this report.

This analysis revealed the following distributions of SmarterMeasure scores:

Individual Attributes Technical Knowledge
Reading Comprehension Overall Technical Comp.


In November, 2010 an analysis was conducted using only data from secondary level students to determine the appropriate readiness ranges settings for the secondary version of SmarterMeasure. Click Here to view a copy of this report.




Learning Styles

The learning styles scale embedded into SmarterMeasure is an original, proprietary assessment based on the multiple intelligences approach to identifying a person's dominant learning style(s).

Read more about learning styles ...



Individual Attributes

The scale of SmarterMeasure which measures individual attributes is an original, proprietary assessment based on the dissertation research of Dr. Julia Hartman.

In her dissertation she identified the individual attributes which are significant predictors of success in an online learning environment. These are variables such as motivation, procrastination, time availability, and willingness to seek help. The individual attributes section of SmarterMeasure measures these variables which are indicators of success in an online course environment.

Person Education LogoSmarterMeasure has partnered with the world's leading learning company, Pearson Education, to provide student success resources at a discounted rate. Through using these resources students can learn how to enhance their opportunities for success in higher education.



Life Factors

The Life Factors scale in SmarterMeasure is an original, proprietary assessment that was designed based on formal and informal feedback which was submitted by faculty and administrators of several schools which use SmarterMeasure.



On-Screen Reading Rate and Recall

The on-screen reading rate and recall scale of SmarterMeasure is an original, proprietary assessment which was developed by an expert panel of educators representing institutions which are clients of SmarterServices in cooperation with LiteracyWorks.org which is a project of the National Institute for Literacy.

Both reading rate and recall are measured in SmarterMeasure because students should realize that they must not too rapidly read on-screen course content because they may be assessed on the content in their courses.

SmarterMeasure is used by secondary schools, technical colleges, community colleges, universities and corporations. To best fit the needs of the learners of each of these organizations, several reading passages are available. Institutions using SmarterMeasure may select per login group the reading passage that is most developmentally appropriate for that group of learners. The following passages are available:

Grade Level Topic Flesh-Kincaid Grade Level Flesh Reading Ease Number of Words
8 Pencils 8.3 59 471
9 Cell Phones 9.8 52.4 510
10 Neil Armstrong 10.1 53.8 635
11 Information Literacy 11.5 38.7 414
12 Batteries 12.9 42.8 652
13 Contact Lenses 13 40 720

The Flesch/Flesch-Kincaid Readability Tests are designed to indicate comprehension difficulty when reading a passage of contemporary academic English. The two tests are the Flesch-Kincaid Grade Level and the Flesch Reading Ease. Although they both use the same core measures (word length and sentence length), they have different weighting factors, so the results of the two tests correlate imperfectly: a text with a comparatively high score on the Reading Ease test may have a lower score on the Grade Level test. Both systems were devised by Rudolf Flesch.

The "Flesch-Kincaid Grade Level Formula" translates the 0-100 score to a U.S. grade level, making it easier for teachers, parents, librarians, and others to judge the readability level of various books and texts. It can also mean the number of years of education generally required to understand this text. The result is a number that corresponds with a grade level. For example, a score of 8.2 would indicate that the text is expected to be understandable by an average student in 8th grade (usually aged 13-14 in the U.S.).

In the Flesch Reading Ease test, higher scores indicate material that is easier to read; lower numbers mark passages that are more difficult to read. For comparison the Readibility Index of the Reader's Digest is about 65, Time Magazine is about 52 and the Harvard Law Review is in the low 30s.

The degree to which the learner can recall the information in these passages is measured by ten questions. There are two of each of the following types of questions: sequence of events, factual, inferential, cloze, and main idea.

Participants are not allowed to view the reading passages while taking the quiz. As such SmarterMeasure provides an assessment of reading recall, not reading comprehension. The intention of this component of SmarterMeasure is to measure the degree to which a person can read academic information on-screen and then recall that information on a quiz. This is a task that is frequently replicated in online and technology rich courses.

It should be noted that the reading rate and recall section of SmarterMeasure should not be used as an exhaustive reading skills inventory. Rather, it should be used as a screening device to identify learners who may be having difficulty recalling what they have read on-screen. If a learner is identified as having opportunities for growth in this area, the school can then inform the student about the resources for remediation and support which they provide. Communicating these resources can be automated through the feedback mechanisms of SmarterMeasure.

National Institude for Literacy Banner

Literacy Works Banner



Technical Competency

The technical competency and typing scales of SmarterMeasure are original, proprietary assessments and were initially developed by Dr. Mac Adkins. Dr. Adkins holds an Ed.D. from Auburn University in Educational Leadership with an emphasis on instructional technology. Dr. Adkins was one of the authors of the Alabama Course of Study in Technology used by all public schools in Alabama. He was also a participating writer for the National Education Technology Standards (NETS) for Teachers document published by the International Society for Technology in Education. Dr. Adkins also teaches Administration and Leadership of Distance Learning Programs online for Capella University. Since the initial iteration of these scales they have been revised numerous times by input from schools which are using the assessment.

The premise of the technical competency section is that if students do not possess basic technical competencies, they will quickly become frustrated and may drop out of the online course. The tasks measured in the technical competency section are basic technology skills which a learner should possess to begin studying online.



Typing Speed and Accuracy

Average typing speeds of persons who type regularly in their occupation range between 50 to 70 words per minute. Average typing speeds for the general public are considered to be around 30 words per minute. Between July 1, 2009 and June 30, 2010 a total of 152,130 students completed the typing section of the SmarterMeasure assessment. The average adjusted typing speed of these students was calculated to be 27.64 words per minute. This slower average rate of typing is a factor that should be considered by schools as they design online courses and by students as they plan for their time to participate in online courses. The formula used to calculate the average adjusted typing speed among students who took SmarterMeasure was to divide the number of words by the number of seconds and subtract for the number of errors.

The scales that measure Typing Speed and Accuracy are original, proprietary skills tests that were internally designed.

Adjusted words per minute

Adjusted Typing Speed
N 152,130
Mean 27.64
Median 26
Mode 21
Standard Deviation 11.997
Decile (10%) Typing Scores
1st top 10% 44+ WPM
2nd 10% 37 - 43 WPM
3rd 10% 33 - 36 WPM
4th 10% 29 - 32 WPM
5th 10% 26 - 28 WPM
6th 10% 23 - 25 WPM
7th 10% 20 - 22 WPM
8th 10% 17 - 19 WPM
9th 10% 13 - 16 WPM
Bottom 10% 12 or less WPM

Although the average adjusted typing speed of these students is lower than the general public, this may partly be explained by high levels of typing accuracy. The nature of academic assignments prompts students to be more concerned with typing accuracy than speed since inaccurate words could negatively impact their grades on the submitted assignments. On a scale of 0 to 100% the average typing accuracy of these 152,130 students was 92.41%.

Typing Accuracy
N 152,130
Mean 92.41%
Median 98%
Mode 100%
Standard Deviation 16.956




SmarterMeasure Usage Patterns

Schools use SmarterMeasure in a variety of ways. A common model is that schools embed SmarterMeasure as an assignment into their orientation course. Some schools use it in the orientation course which is specific to online learners while other schools use it in the general orientation course which all students take. SmarterMeasure is a useful student service tool not only for students who will be taking fully online courses, but also hybrid courses, video conferencing courses and even face-to-face courses which use the Internet for communication in the course. Several schools make SmarterMeasure available to prospective students through their website. In May, 2009 schools which use SmarterMeasure were asked to describe how that SmarterMeasure is beneficial to their students and how they utilize SmarterMeasure. Click here to view this report.





Brief Review of Literature on the need for SmarterMeasure

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 proficiency and a strong motivation to learn by technology (Hsiu-Mei & Liaw, 2004). 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.

Click here to view a paper presented at the 2009 Distance Learning Administration conference about the usage of SmarterMeasure.



Developmental Students

When developmental students enroll in distance classes, they bring with them the same need for support that they have in a conventional classroom (Caverly and MacDonald, 1998; Rhoda and Burns, 2005), and surprisingly little research has been done on how best to facilitate the progress of underprepared students in an online class (Perez and Foshay, 2002). Distance education requires more self-directed learning and higher levels of personal motivation, independence and self-discipline (Sampson, 2003), in addition to the technical skills required for participation in an online class (Caverly and MacDonald, 1998). These are all skills in which underprepared students might be lacking. Fortunately, the same technology that delivers the class can deliver the support systems.



Additional Research Requests

Additional research on SmarterMeasure is welcomed. If you are interested in conducting research on the topic of online student readiness using SmarterMeasure data please send a brief research request to Dr. Mac Adkins. In the research request describe the purpose and plan for your research including the proposed subjects, timeline, and plans for the dissemination of the research. All research done using SmarterMeasure data must meet our privacy statement. We never release to third parties any data which identifies individual or other school specific data.



Reference List

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