Measures of Effectiveness (MoEs) for MarineNet: A Case Study for a

Smart e-Learning Organization

Ying Zhao

, Tony Kendall and Riqui Schwamm

Naval Postgraduate School, Monterey, CA, U.S.A.

Keywords: Measures of Effectiveness, MoEs, Distance Learning, Online Learning, Personalized Learning.

Abstract: MarineNet is an US Marine Corps system that provides one-stop shop and 24/7 access to thousands of online

courses, videos, and educational materials for the whole Marine Corps. The need for the e-learning

organization is to identify the significant capabilities and measures of effectiveness (MoEs) for appropriate e-

learning, and then design and identify how to collect and analyze the big data to achieve an effective

integration of analytic within the MarineNet learning ecosystem. We show this as a use case and the sample

data of the MarineNet CDET website on how to design MoEs that can guide how to collect big data, analyze

and learn from users’ behavior data such as clickstreams to optimize all stakeholders’ interests and results for

a typical e-organization. We also show the processes and deep analytics for exploratory and predictive

analysis. The framework helps e-organization determine where investment is best spent to create the biggest

impact for performance results.

1 INTRODUCTION

MarineNet is an US Marine Corps system that

provides one-stop shop and 24/7 access to thousands

of online courses, videos, and educational materials

for the whole Marine Corps. Many MarineNet

courses meet specific Marine Corps training

requirements and are extensions of resident schools.

Other MarineNet courses are commercially

developed and licensed to support individual skill

development.

We initially investigated the College of Distance

Education and Training (CDET)’s current content

management systems (CMS) and their future needs

(MarineNet, 2018). The first need is to 1) identify the

significant capabilities and measures of effectiveness

(MoEs) for appropriate electronic learning (or

distance/distributed learning); 2) design and identify

how to track this data, and proper analytic techniques

to achieve an effective integration of analytic within

the MarineNet learning ecosystem. The MoEs of the

learning outcomes for MarineNet users reside in the

following four specific areas:

• User Profiles

• Courseware

• Video Services

• Site Collaboration

https://orcid.org/0000-0001-8350-4033

Analytic methodologies are needed in the following

areas

• Data Capture

• Dashboard

• Machine Learning

• Predictive analytics

Part of the research questions are listed as following:

1.What constitutes appropriate measures of

effectiveness (MoE) for training and education

distance learning materials in an enterprise level

collaboration learning environment?

2. As a distance learning website with many different

stakeholders (e.g., students, instructors, sponsors, and

developers), what data need to be collected to support

the identified MoEs and support the total value of the

website and business processes?

3. What analytic attributes are essential for CDET to

collect, analyze, and present useful information in a

real-time, intuitive, adjustable, and visual manner

(dashboard) to support the identified MoEs?

4. How can the CMS support a dashboard that allows

for data manipulation, aggregation, and visualization

of identified MoEs?

In this paper, we show how to answer these

research questions. Answers to the research questions

are related to a broader research area such as data-

driven education (Boudett, City, & Murname, 2013;

146

Zhao, Y., Kendall, T. and Schwamm, R.

Measures of Effectiveness (MoEs) for MarineNet: A Case Study for a Smart e-Learning Organization.

DOI: 10.5220/0008480701460156

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 146-156

ISBN: 978-989-758-382-7

Dunlosky, Marsh, Nathan, & Willingham, 2013)

clickstream analysis (Nasraoui, Cardona, Rojas &

Gonzalez, 2003), effectiveness of online learning or

Massive open online course (MOOC, 2019;Kaplan &

Haenlein, 2016; Balakrishnan, 2013; Hew, 2016),

content management (CMS, 2018), and high level

cognition and learning models (Heer, 2010;

Kirkpatrick, 2019).

2 DESIGN MEASURES OF

EFFECTIVENESS (MoEs) AND

DATA COLLECTION

We see the potential for the MarineNet platform to

deliver personalized learning including micro-

learning--targeted learning either for certain groups, a

specific student, or perhaps identified learning styles

of the student. The technology can support

personalized learning through Experience API (xAPI,

2019)— an e-learning software specification that

allows learning content and learning systems to speak

to each other in a manner that records and tracks all

types of learning experiences. However, the

technology isn’t the full solution because content

must support learning outcomes/objectives and

learning must be assessed to measure and improve the

learning process. This is accomplished first by

designing valid measures of effectiveness (MoEs). To

answer the research questions, we studied current

learning theories and, using existing and available

MarineNet data, designed and selected MoEs to

support those constructs based on accepted

pedagogical theory and practice as well as on our

exploration and evaluation of various deep analytics

models.

Initial research identified 36 MoEs, among them,

content profiles, student profiles, and student learning

behavior are the most important categories as follows:

1 Content Alignment Data.

At the enterprise level, measures of program

effectiveness (to a lesser extent learning) are typically

tracked by completion rate, GPA, and stated course

outcomes or objectives. A CMS must capture this

information first. One consideration is that course

content must align with the course objectives which

could greatly impact the course outcomes (e.g.

completion rate and GPA) and provide students

suitable learning experiences, appropriate

assessment, and measurable progress.

For example, a MoE in this category can compute

the correlation of a course content with its predefined

objectives. However, this may require text analysis

which is out of the initial scope of this project. As an

alternative, an instructor could tag the various content

such as pre-tests, progress tests, and post-tests with

the corresponding learning objectives so that the test

scores, GPA, and complete rates can accurately be

measured if the learning objectives are achieved. If

the instructor is trained to develop

objectives/outcomes for higher levels of learning for

example, critical thinking can be measured using

content tags as well.

2 Student Profile Data.

Measures for learning are often unique to disciplinary

fields or individual cohorts or communities.

Measuring learning must consider the level of

knowledge the student has before the course as

compared to after the course, the delta. This delta

would then measure the transference of new

knowledge not what the student already knows. These

MoEs can be supported by a pre-course survey to

collect demographic, biographical information.

Motivation reflected in the information can

significantly enhance retention and transfer and can

be the unique differences among individual learners.

3 Student Learning Result Data.

A MoE in this category can be the degree to which

targeted outcomes occur as a result of the training, for

example, grades, rubric, rewards, GPA, and the

number of attempts for competency-based quizzes—

i.e. required score of 80%? For example, MarineNet

Distance Education Programs and training courses

typically use 80% to demonstrate the required

knowledge master level.

4 Student Learning Behavioral Data.

Through the MarineNet website, a student can

interact with content, instructors, and peers. These

interactions can be recorded as student learning

behavior data.

The average time and frequency that students

access different content are the important measures of

student learning behavior. A MoE in this category can

be the frequency (or clickstream patterns) for each

type of content accessed.

Since an instructor could tag that content (or not)

that he or she deems that repetitive learning would be

beneficial. MoEs in this category could be aggregated

counts or percentages if tagged by objectives or by

type of content.

Interaction with instructors with guided practice

and timely, formative feedback improves learning

and performance. A MoE in this category can be the

frequency (or clickstream patterns) for such

interactions: the number of times (counts)

instructors/students communicate through email

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(recorded by the system) or forums and blogs. The

quality of the interaction is also important for

example such as the duration of interaction.

Interaction with other fellow students measures if

active participation of the online communities,

discussion forums, and group studies exist for a

student. Educators indicate there is a strong

correlation between learning results and the level of

interaction among the students.

With the MoEs and corresponding big data from

the website pages (clickstream counts, eyeball time),

we apply different types of analytics such as

exploratory analysis, visualization in a dashboard,

and predictive analytics to discover basic patterns and

trends towards a framework for personalized learning

as in Figure 1, showing how big data and analytics

can be used for personalizing MarineNet learning by

recommending personalized materials.

The framework helps CDET determine where

investment is best spent to create the biggest impact

for learning.

3 IDENTIFY TOOLS

In order to perform data analytics and build machine

learning models, we first identified various big data

and deep learning tools such as Tableau, Orange,

Jump, MATLAB, D3, Python SciPy, Plotly, Pandas,

NetworkX, RapidMiner, R, Octave, WeKa, and

Google Analytics. We have tested the tools and

processes using an open source online learning data

set (NIH data set, 2018, consisting of 22 courses, 32K

students and daily summaries of student clicks (about

10 million entries).

Since as an organization providing e-learning for

a government entity such as the Marine Corps,

MarineNet cannot store data in a commercial cloud

and therefore requires secure and cost-effective

analytics for the continuous analysis of the enterprise.

Free and standalone tools are recommended for

research and developers can later integrate the

research results coded in Python into the production

system.

4 DATA EXTRACTION AND

PRE-PROCESSING

Part of the MarineNet currently uses Moodle

(Moodle, 2019) as the distance learning management

system (LMS). We obtained sample data from the

existing Moodle system, which does not contain the

comprehensive required data elements for MoEs,

however, it does include the essential student learning

results (part of student profile data) and learning

behavior, i.e., the interaction with the website

contents for a few courses. We used the data sets to

validate part of the MoEs and analytic process to

integrate deep analytics including exploratory data

analysis, visualization, predictive models with a few

machine learning and artificial intelligence models

Figure 1: MarineNet avatar to leverage big data and machine learning mdoels for personalizing MarineNet materials.

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with an MoE focus. Ultimately the results will indicate

to the learner recommended content for an

individualized learning path and a course of action for

mediation of training objective knowledge

discrepancies.

Moodle data from three different classes were used

(BCOC 1-19, LCC 1-19, and LCC 3-18). There were

two types of data highlighting the student learning

behavior (class logs) and student profile (grades) that

were extracted. Figure 2 shows an example of class

logs and how many events (via different names) were

extracted. Figure 3 shows number of events listing

what method and how frequent each student interacts

with the website. Figure 4 shows the grades data

containing the quiz grades and final grade for the class.

The grade data also included numbers of forums and

discussions participated by students. The class logs and

grades were joined using the user names. The user

names have been anonymized before any data pre-

processing. The final student grade were put into three

bins: ‘3’ represents the grades greater than the mean

plus one standard deviation; ‘2’ represents the grades

between the mean minus one standard deviation and

the one plus one standard deviation; ‘1’ represents the

grades less than the mean minus one standard

deviation; and ‘0’ representing no grade.

Figure 2: An example of class logs, how many events (via different names) were extracted.

Figure 3: Number of events showing the frequency and methods for each student interacted with the website.

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Figure 4: The grades data contain the quiz grades and final grade for the class. They also included numbers of forums and

discussions participated by students. The class logs and grades were joined.

5 EXPLORATORY DATA

ANALYSIS (EDA)

The dataset included three MarineNet courses

consisting of events such as “content page viewed.”

Event logs and grades were merged by user name.

Logs and grades from the three different courses were

used. Number of events per user were listed and basic

relationships between events were compared to

performance (learning) as measured by final grade

which is used as the dependent variable. The candidate

independent attributes (student activity) included:

 File uploaded

 A submission submitted

 Comment created

 Content page viewed

 Course module viewed

 Course viewed

 Discussion viewed

 Post created

 Question answered

 Some content posted

 status of submission viewed

 …

Total about 120 of the different events and aggregated

attributed were extracted from the data.

The four independent variables of student activity

selected for initial EDA: Content page viewed, Course

module viewed; Course viewed, and Discussion

viewed. These four attributes had the highest

coefficients of variation (CV) and were selected

because the higher variability may be the source of

variability with student grades and therefore possibly

correlated. Those other variables with lower CVs and

very narrow dispersion about the mean, with almost

zero SD, would unlikely result in meaningful

correlations with course grades.

The example below uses the data set BCOC1-19

since it has the greatest number of students (84 total

students and 64 had grades in the end of the class).

Tableau.

Tableau is a cloud-based business intelligence for

enterprises (Tableau, 2019), which allows many

different views and visualizations of a data set. We

used a desktop version of Tableau which is limited in

terms of big data size and does not use cloud

computing.

Figure 5 shows a Tableau view of number of

specific events (size of the rectangles and final grades

(color) for each student. Different learning behavior

exhibited here: Student 8826 has a higher grade

(darker) and large number of events of “Course

viewed” and “Course module viewed;” Student 6466

has a lower grade (lighter), however fewer number of

events of “Course viewed” and “Course module

viewed;” Student 2293 has a higher grade (darker),

however, fewer number of “Course viewed.”

Sankey.

Sankey diagrams ( Sankey, 2019) are a specific type of

flow diagram, in which the width of the arrows is

shown proportionally to the flow quantity. This

extends the capability of the Tableau view above to

view and visualize more attributes in the data set.

Figure 6 shows a Sankey plot for a class log. The

attributes from left to right are “user name,”

“component,” “event name,” and “grades”. There are

6.91k events linking “Course module viewed” and “3

(Grade > mean + std).” The graph shows initial

correlation between the higher grade (3) and the

learning behavior “Course module viewed.”

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Figure 5: A Tableau view shows number of specific events (size of the rectangles and final grades (color) for each student.

Different learning behavior exhibited: user 8826 has a higher grade (darker) and large number of events of “Course viewed”

and “Course module viewed;” Student 6466 has a lower grade (lighter), however fewer number of events of “Course viewed”

and “Course module viewed;” Student 2293 has a higher grade (darker), however, fewer number of “Course viewed”.

Figure 6: Sankey plot for a class log. The attributes from left to right are “user name,” “component,” “event name,” and

“grades”. There are 6.91k events linking “Course module viewed” and “ 3 (Grade > mean + std)”.

MATLAB.

Exploratory analysis tools included MATLAB

(Matlab, 2019), Excel Analysis ToolPak and the open

source tool, Orange. MatLab includes multiple

regression as well as other powerful exploratory tools

but for an educational analyst the Excel Analysis

ToolPak provides a quick an easy way to initially

explore the data. Multiple regression analysis of the

four attributes with the dependent variable of final

course grade resulted in an R square of .13 and an

adjusted R square of .08. Simple regression shows

“Course Viewed” with the highest R Square of .12

(adjusted R square of .11) which is low but it is

intuitive that one must view the course more than

once to learn the content (at least for some students.

There were only 65 observations and the analysis goal

was to demonstrate how generated data from an LMS

could be initially analyzed. Figure 7 is an Excel

residual plots which shows a probable non-

randomness suggesting a better fit for a non-linear

model.

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Figure 7: Excel residual plots.

Figure 8: Orange Tool (Pearson correlation).

Orange.

Another exploratory tool used was Orange (Orange,

2019). It features a visual programming front-end for

explorative data analysis and interactive data

visualization. Orange consists of data and pre-

processing tools including importing relational and

unstructured databases, filtering, merging, data

sampler, and discretizing. Visualizing tools include

statistical functions such as box plots, distributions,

scatter plots, and heatmaps. Orange includes

implementations of many advanced statistical and

machine learning algorithms such as linear and

logistic regression, decision trees, naïve Bayes,

random forest, and neural networks. One useful data

tool in Orange is Pearson’s correlation, which quickly

identifies correlations between many attributes.

Figure 8 shows “content page viewed” and “course

module viewed” are almost synonymous and

therefore one or the other could probably not be used

in the analysis.

6 PREDICTIVE MODELS

In order to improve online learning to help students

achieve the best results, we need to predict a student’s

end result (e.g., report) before the end of the class,

identify the reasons for predictions, and then send

early warnings or personalize the content so the

student can improve their behavior or obtain more

personalized content, and therefore receive better

learning results.

Figure 9 shows an Orange workflow for building

typical machine learning models including decision

trees (Weka, 2019), neural networks, kNN, naive

Bayes, and logistic regression for predictive models

of grades. Figure 10 shows the decision tree output

for the total 83 students in BCOC 1-19. The goal of a

predictive model is to predict grade level 1, 2, and 3

based on the students’ interactions/activities (events)

in the website, where 1: total grade scores <520, 2:

640> total grade scores>520; 3: total grade scores

>640. The decision rules for the two green box (i.e.,

the highest grade bin “3”) are as follows

 “Number of events for a file uploaded >0”,

“course viewed >395,” “Quiz attempt submitted

>15;”

 “Number of events for a file uploaded >0”,

“course viewed >395,” “Quiz attempt submitted

<=15,” “Course viewed <=406.”

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Figure 9: An Orange work flow for building models.

Figure 10: The decision tree output for the students in BCOC 1-19. The decision rules for the two green boxes (i.e., the highest

grade bin “3”) are 1) “Number of events for a file uploaded >0”, “course viewed >395,” and “Quiz attempt submitted >15;”

2) “Number of events for a file uploaded >0”, “course viewed >395,” and “Quiz attempt submitted <=15,” “Course viewed

<=406”.

The decision rules for the two blue boxes (i.e. the

lowest grades bin “1”) are listed as follows:

 “Number of events for a file uploaded >0”, “course

viewed <=395,” and “Zip archive of folder

downloaded > 4;”

 “Number of events for a file uploaded >0”, “course

viewed <=395,” “Zip archive of folder downloaded

<= 4;” and “Course viewed >=375.”

Note that the rules for the low performers compared to

the high performers seem different in terms of the

attributes “Course viewed” and “Zip archive of folder

downloaded.” The zip archive could be the quiz

materials on which the students tried to work. Proactive

actions may be taken, for example, to remind students

to finish the quizzes on time before they attempted too

many times. The decision trees algorithm

automatically discovered the rules and thresholds used

in the rules.

The question is that if these rules apply to the test

set (e.g., LCC-1-19 in Figure 9). We scored the test set

using the output rules from the training set (i.e.,

BCOC-1-19) to generate predictions of high (3), low

(1), and average grades (2) for the students in this class

and then compared the predictions with the ground

truth.

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(a) (b)

Figure 11: (a) An Orange work flow for building multiple predictive models; (b) Test & Score shows a 20-fold cross-

validation of classification accuracy (CA), precision (% of true positives out of predicted), recall (% of true positives out of

actual).

(a)

(b)

Figure 12: (a) Confusion matrix of the cross-validation model for the decision trees method; (b) Confusion matrix of the

cross-validation model for the neural network method.

Since the number of students in each class is low and

the classes are quite different, a low accuracy of test

prediction is expected. Nevertheless, the machine

learning algorithm can pick up more relevant

attributes.

We examined more predictive methods using

class BCOC-1-19 data and taking out 18 students

without grades which left only 65 students in the data

set. We used the cross-validation method for these

predictive models which splits the data into a given

number of folds (20 folds in this case).

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Figure 13: Future work combining contents and learning models.

The algorithm is tested by withholding examples

from one fold at a time; the model is induced from

other folds and examples from the held out fold and

classified. This is repeated for all the folds (Orange,

2019). The fused grades and logs result in 119

variables in total. Figure 11 (a) shows the Orange

workflow for multiple predictive models. Figure 11

(b) shows the a 20-fold cross-validation results of

classification accuracy (CA), precision (% of true

positives out of predicted), recall (% of true positives

out of actuals) for multiple predictive models. The

predictive models have similar results except the

naïve Bayes method. Figure 12 (x,y axes grade level)

shows detailed confusion matrices for the decision

trees (a) and neural networks methods (b). The

decision trees method has a better confusion matrix

(errors confuse “2” with “3” and “1” and “2”) than the

neural networks method only predicted 20% correctly

for the grade level 3.

7 DISCUSSION

We show an example using the sample data of the

MarineNet CDET website on how to design MoEs

that can guide on how to collect big data, analyze

online behavior data such as clickstreams correlated

with performance assessment data, therefore measure

and improve all stakeholders’ interest and results for

an e-learning organization. Many of the analytical

tools indicate that useful analytics and predictive

power can be derived from website logs and

assessment data. To extend the project for a future

prospective for distance e-learning and the MoEs we

develop in this project, one can leverage more

technology to support personalized learning and

collect student learning behavior data such as

Experience API (xAPI, 2019).

More challengingly in terms of measuring how

students learn, we could use more content based

MoEs which are not of the focus of this paper. The

related subset of MoEs were based on accepted

pedagogical theory and practice as well as on our

exploration and evaluation of various learning models

that may measure learning or training or at least

measure some of their correlations. For example, how

to measure Bloom’s Taxonomy (Heer, 2010) related

data, which classifies learning into factual,

conceptual, procedural, and metacognitive and the

subsequent cognitive dimensions required, and it is a

classic learning model and very in use today. MoEs

related to the Kirkpatrick Model (Kirkpatrick, 2019)

focus on the degree the learner interacts with the

content in each of the four levels, for example, 1)

Reaction: degree to which training is favorable,

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engaging and relevant to their jobs; 2) Learning:

degree to which participants acquire intended

knowledge, skills, attitude, confidence and

commitment based on their participation in the

training; 3) Behavior: degree to which participants

apply what they learned during training; 4) Results:

degree to which targeted outcomes occur as a result

of the training. Figure 11 shows a holistic view of

potential future work of the integration of the

concepts and models for total personalized smart

online learning leveraging big data and machine

learning.

8 CONCLUSION

We show a use case using the sample data of the

MarineNet CDET website on how to design MoEs

that can guide how to collect big data, analyze

website behavior data such as clickstreams with

performance assessment data, therefore measure all

stakeholders’ interests and results for an e-learning

organization. We also show the processes and tools

for exploratory and predictive analysis.

ACKNOWLEDGEMENTS

Thanks to the support of the Naval Research Program

at the Naval Postgraduate School and the College of

Distance Education and Training (CDET) at the US

Marine Corps University (USMCU). The views and

conclusions contained in this document are those of

the authors and should not be interpreted as

representing the official policies, either expressed or

implied of the U.S. Government.

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