RSHUM 806Literature Review Grading RubricStudent Criteria.docx

RSHUM 806
Literature Review Grading Rubric
Student:
Criteria
10 points
8 points
6 points
4 points
0 points
Points Earned
Abstract
The abstract is a brief, focused description of the contents of
the paper
Generally focused on the contents of the paper
Somewhat focused on a broad topic
Scattered, random writing without focus
N/A
Introduction
Clearly and concisely articulates the topic of study, states the
research question(s) motivating the investigation, and discusses
the theoretical/conceptual framework and historical background
for the study
Discusses the topic of study, the research question(s), and the
theoretical/ conceptual framework or historical background for
the study
The topic of study and research question(s) are unclear. The
framework and background for the study are vague
Unfocused and wandering discussion; missing multiple required
elements
N/A
Discussion of Key Terms

Keywords and essential terms are clearly discussed and defined
using direct support from authoritative sources on the topic;
includes citations
Keywords and essential terms are clearly discussed and defined
Definitions of keywords and essential terms are ambiguous or
vague
Missing multiple required elements
N/A
40 points
30 points
20 points
10 points
0 points
Review of the Literature
Relevant research findings are tightly synthesized and organized
by themes/categories; uses a balanced amount of direct
quotation; includes citations to support findings
Research findings are organized by themes/categories; uses
direct quotations and includes citations to support findings
Research findings are summarized by study, rather than
synthesized by themes/categories; direct quotations are used
either too heavily or too sparingly
Fails to include relevant research or includes findings unrelated
to the topic; sources of argumentation and support unclear
N/A
10 points
8 points
6 points
4 points
0 points

Summary/ Conclusion
Includes a focused summary of key findings from the review;
gaps in the literature and recommendations for future research
are clearly discussed
Includes a summary of key findings from the review; gaps in the
literature and recommendations for future research are
identified
Summary of key findings is unfocused or inconsistent with the
review; gaps in the literature or recommendations for future
research are vague
Unfocused and wandering summary; missing multiple required
elements
N/A
Grammar, Usage, & Mechanics
0–2 errors
3–4 errors
5 errors
6–8 errors
More than 8 errors
APA Format
0–2 APA errors
3–5 APA errors
6–7 APA errors
8–9 APA errors
More than 10 APA errors
Total:
Instructor Comments:
Gradaute Rearch Course Dr. Arbelo

Writing Guide for the Literature Review
I. Prewriting involves the preparation and arrangement of your
ideas before writing them into a paper. Use whatever
techniques work for you (e.g.–Freewriting, Brainstorming,
Listing, Outlining, Questioning, Clustering). Your research and
documentation are accomplished during the prewriting stage.
A. Sources
1. Generate material from outside sources. You must use at
minimum 10 outside sources for your literature review. These
sources MUST be credible, quality academic sources that are
limited to: peer reviewed journal articles (mostly), textbooks,
government websites.
2. Peer-reviewed sources are preferred (journals and books
published at university presses). You can find such sources
through Liberty’s online library. The Library Research Portal
there will help you find scholarly journals. Note that the top
database used for education subjects is ERIC.
3. Because this research assignment has many possible facets
you can explore, you may have a valid reason for using a non-
peer reviewed sources. Exceptions include the following:
a. Online databases of historical texts/documents (where the
sponsoring organization, editorial board, and information about
the original printed source are clearly identified)
b. Professional organizations (usually ending in a .org suffix)
c. Government agencies (ending in a .gov suffix)
d. Websites with the “.edu” extension are not necessarily
reliable ones as many different people have access to posting
articles on such sites. Additionally, faculty material published
on such sites have not been subject to the rigorous review
process required by print publications (Just because someone
has the degree doesn’t mean his/her entire body of work is
recognized by the academic discipline in which he/she
operates).
B. Research

a. Go through your sources and take notes on information
relavent to your topic.
b. Be creative & original in selecting information.
c. Once you’ve discovered your purpose for writing, that
purpose will inform the rest of your note taking.
d. Document! When you’re getting ideas from outside sources,
you must take special pains to identify that material as such, so
record all bibliographical information and make note of the
page source of every quotation you retrieve. Doing this now, at
the prewriting stage, will save you much grief later on.
II. Thesis Statement: For this assignment, you should have
already written your thesis statement.
III. Outline and write your literature review. The way you
synthesize material is unique to you. Your arrangement of
various points can be original. Your own interpretations and
ideas can be incorporated. Look for gaps in your sources: there
may be a point that is not stressed or an obvious conclusion that
is overlooked. Dispute with your sources. Remember to stay in
line with your Research Proposal as well as the guidelines
provided by the Writing the Winning Dissertation textbook.
IV. Revise, Edit, and Proofread
A.
Check your thesis statement. Does it clearly articulate all the
points you’ve covered in your review? Are any points
mentioned that aren’t covered in your review?
B.
Check your body paragraphs against your thesis (12 page
review). Are they related to your thesis? Are they analytical?
C.
Check the details of your body paragraphs. Do you have
enough support for your topic sentence? Are all the details in
each body paragraph directly related to their respective topic
sentence? Are the points you’re making arranged in such a way

that your reader can clearly follow your line of thinking? Do
you have too much outside support (so much so that it
overwhelms your voice)?
D.
Read your paper carefully (out loud is suggested).
E.
Check your compliance with APA format. Review in-text
documentation and works cited page (using APA Handbook to
do so). Check for any missing citations; fix if necessary.
V. Submit the final draft of your paper by the deadline stated
(12 page literature review, not including the cover page or
reference page).
FOLLOW THIS LAYOUT: Introduction, Conceptual
Framework, Review of the Reseach Literature, Summary and
Future Research
Introduction
A sound literature review is an extremely important component
of many types of papers written in graduate school. Professional
journals across disciplines typically require authors to include a
literature review in articles that are submitted for consideration
for publication. Students are asked to include a literature
review in theses and dissertations, as well as papers across
graduate school courses. Writing a properly structured
literature review is a very important skill at the graduate level.
A literature review (also expressed as “a review of the
literature”) is an overview of previous research on the graduate
student’s topic. It identifies and describes and sometimes
analyzes related research that has already been done and
critically summarizes the state of knowledge about the topic.
To best understand the role of a literature review, consider its
place in the research process and in the research paper.

The research process often begins with a question that the
researcher would like to answer. In order to identify what other
research has addressed this question and to find out what is
already known about it, the researcher will conduct a literature
review. This entails examining scholarly books and journal
articles, and sometimes additional resources such as conference
proceedings and dissertations, to learn about previous research
related to the question. Researchers want to be able to identify
what is already known about the question and to build upon
existing knowledge. Familiarity with previous research also
helps graduate students understand a topic in an in depth
manner and it also helps researchers design their own study
Literature review should include:
Abstract – no more than 150 words properly formatted
A. Introduction to the Literature Review
B. Conceptual Framework
C. Review of Research Literature
D. F. Summary and FutureResearch
The Objectives of a Literature Review
Authors should try to accomplish the following four important
objectives in preparing a literature review:
1. The review should provide a thorough overview of previous
research on the topic. This should be a helpful review for
readers who are already familiar with the topic and an essential
background for readers who are new to the topic. The review
should provide a clear sense about how the author’s current
research fits into the broader understanding of the topic. When

the reader completes reading of the literature review, she or he
should be able to say, “I now know what previous research has
learned about this topic.”
2. The review should contain references to important previous
studies related to the research question that are found in high
quality sources such as scholarly books and journals. A good
literature review conveys to readers that the author has been
conscientious in examining previous research and that the
author’s research builds on what is already known. In this
process, highly interested readers are also provided with a set of
references that they may wish to read themselves.
3. The review should be succinct and well-organized. Most
literature reviews range from 30 to 80 pages. In this assignment,
you are going to develop a literature review of 20 to 25 pages.
This is not including cover page, abstract, and references.
Every page should be well developed, succinct, and follow the
guidelines.
Many authors like to begin with an “Introduction” section that
identifies the general topic and its importance. This is followed
by the “Literature Review” section that provides the overview
of previous research and explains what has and what has not
already been learned. Much of the focus of the literature review
is on previous research related to the subject under study. This
includes most recent research over the past 7 years, theories,
evidence best practices, common findings among studies,
critical areas that still need research.
4. The review should follow APA Guidelines 6th edition.
Sections to be developed:
Introduction:

The introduction to the LR sets the stage by describing the
boundaries of the literature search (LS) within the field of
study. The researcher’s LS gathers, analyzes, and synthesizes
research articles, research reports, seminal books, governmental
and institutional reports, historical documents, archival media,
and so on, to discover and present the state of knowledge
concerning the topic under study. Available time, critical
skills, prior knowledge, and scholarly discernment allows the
researcher to know how far and wide to search to support their
research. The search must, in the end, meet the twin aims of
establishing 1) comprehensive coverage of the literatures
pertinent to the problem and 2) relevance in the selection and
application of literatures. Since research problems exist within
determinate and specified social contexts and since bodies of
literatures attach to these problems, the research problems
themselves offer contextual and literature boundaries for the
LS.
Machi and McEvoy (2012) suggest the introduction should
contain “six sections . . . (1) the opening, (2) the study topic,
(3) the context, (4) the significance, (5) the problem statement,
and (6) the organization” (p. 145). These sections are designed
to orient the reader to the problem-at-hand and provide an
outline of how the LR argument will be pursued. A good
academic introduction provides the reader with a clear
description of what lies ahead of them and the specific
reasoning steps the author will use to take the reader through to
a logical end. Graduate students are advised to use Machi and
McEvoy’s sections as sub-headings in their Literature Review—
adapting and modifying the headings to meet the needs of their
individual study and LR argument. (See Machi and McEvoy text
for descriptions of the sub-sections listed here.
Conceptual Framework:
The use of a conceptual framework as an argumentative

structure for the LR is very important. Conceptual framework
is a term for the epistemological position a researcher uses to
approach a set of phenomena. The position is “conceptual”
because whenever phenomena are experienced, they are
associated with concepts the researcher possesses—that is, the
researcher’s existing knowledge. Concepts “frame” how the
researcher views phenomena, and thus necessarily shape how
the researcher understands what is observed. Conceptual
frameworks display objectivity and creativity by reflecting the
researcher’s position as 1) a member of a scholarly community
of knowledge; and 2) a scholar possessing a unique noetic
outlook on the world, respectively. A researcher’s ideas must
be objectively grounded in well-confirmed concepts employed
by a community of practice, which validates and confirms
knowledge through communal truth-seekingactivities. Yet a
researcher also always possesses ideas which are unique, by
virtue of her or his individual and distinct noetic perspectives
on the world, which allow for creative new associations,
applications and developments of concepts. The interplay
between unique researcher perspectives and communal truth-
seeking activities allows for the creation and confirmation of
new knowledge through creative acts of individuals (and groups
of individuals) that are then validated by the community. The
two-fold insight here is that: 1) not all new ideas are objectively
valid or rationally justifiable to the community of practice; and
2) existing concepts in the community of practice admit of
addition, revision, and even overturning (see Kuhn, 1962;
Feyerabend, 1975).
a conceptual framework is an argument about why the topic one
wishes to study matters and why the means proposed to study it
are appropriate and rigorous. By argument, we mean that a
conceptual framework is a series of sequenced, logical
propositions the purpose of which is to convince the reader of
the study’s importance and rigor. Arguments for why a study

“matters” vary greatly in scale, depending on the appropriate
audience. In some scholarly work, the study may only matter to
a small esoteric community, but that does not change the fact
that its conceptual framework should argue for its relevance
within that community. Finally by appropriate and rigorous, we
mean that a conceptual framework should argue convincingly
that (1) the research questions are an outgrowth of the argument
for relevance [emphasis added]; (2) the data to be collected
provide the researcher with the raw material needed to explore
the research questions; and (3) the analytic approach allows the
researcher to effectively respond to (if not always answer) those
questions. Further, rigor includes not only how a study is
carried out, but also how the methodology itself is
conceptualized. As we will see in Chapter 4 and Chapter 5 in
particular, methodology is neither objective nor value-neutral.
As such, what you study and how you study it ultimately raises
questions about who you are, what kinds of questions you ask,
the assumptions embedded within those questions, and the
extent to which those assumptions are made explicit and, where
appropriate, subjected to critique. (Ravitch & Riggan, 2012, p.
7)
Review of the Research Literature
The review of research literature is the core of the literature
review and provides the evidence-base that your literature
needs. This review must be undertaken with scientific
organization and must employ the researcher’s best analytic and
descriptive skills in order to provide the reader with an accurate
picture of the state of research in the field. Szuchman and
Thomlison (2011) describe three different types of reviews of
research literature: “empirical,” “theoretical,” and “systematic.”
Empirical reviews are a synthesizing summary of prior research
that “presents a state of knowledge in an area” (p. 62).
Theoretical reviews provide critical analyses of the current
theoretical perspectives in the literatures. And systematic

reviews are undertaken in quantitative studies to provide “a
formal synthesis of experimental research studies designed to
explain how particular interventions affect specific outcomes”
(p. 62). The literature review will necessarily involve empirical
and theoretical considerations.
Summary of the Litrature Review
A comprehensive overview of the literature review.
Tips:
VI. The literature review provides a comprehensive and relevant
review of literature that pertains to a topic in the research. The
primary purpose of the literature review is to identify the topic,
problem, research hypothesis, within the framework of previous
research on the topic. The review provides a reasoned argument
to the claim that the research question guiding the study reveals
an area of investigation that is a new avenue of research, or a
research avenue that has not been adequately studied or
sufficiently confirmed. The LR argument uses the analyzed and
critiqued literatures as a base of evidence to justify the
argument’s main claim. The presentation of this evidence base
demonstrates to the reader that the researcher understands the
field of study and the position the current study occupies within
the community of scholarshipPrewriting involves the
preparation and arrangement of your ideas before writing them
into a paper. Use whatever techniques work for you (e.g.–
Freewriting, Brainstorming, Listing, Outlining, Questioning,
Clustering). Your research and documentation are
accomplished during the prewriting stage.
C. Sources
4. Generate material from outside sources. You must use at
minimum 25 sources for your literature review. These sources

MUST be credible, quality academic sources.
5. Peer-reviewed sources are preferred (journals and books
published at university presses). You can find such sources
through Albizu University’s online library. The Library
Research Portal there will help you find scholarly journals.
6. Because this research assignment has many possible facets
you can explore, you may have a valid reason for using a non-
peer reviewed sources. Exceptions include the following:
a. Online databases of historical texts/documents (where the
sponsoring organization, editorial board, and information about
the original printed source are clearly identified)
b. Professional organizations (usually ending in a .org suffix)
c. Government agencies (ending in a .gov suffix)
d. Websites with the “.edu” extension are not necessarily
reliable ones as many different people have access to posting
articles on such sites. Additionally, faculty material published
on such sites have not been subject to the rigorous review
process required by print publications (Just because someone
has the degree doesn’t mean his/her entire body of work is
recognized by the academic discipline in which he/she
operates).
D. Research
a. Go through your sources and take notes on information
relavent to your topic.
b. Be creative & original in selecting information. Understand
methodologies used, common themes, identify gaps in the
research, the theoretical frameworks applied.

c. Once you’ve discovered your purpose for writing, that
purpose will inform the rest of your note taking.
d. Document! When you’re getting ideas from outside sources,
you must take special pains to identify that material as such, so
record all bibliographical information and make note of the
page source of every quotation you retrieve. Doing this now, at
the prewriting stage, will save you much grief later on.
VII. Thesis Statement/Purpose Statement: For this assignment,
you should prepare it beforehand; use it as a heading.
VIII. Outline and write your literature review. The way you
synthesize material is unique to you. Your arrangement of
various points can be original. Your own interpretations and
ideas can be incorporated. Look for gaps in your sources: there
may be a point that is not stressed or an obvious conclusion that
is overlooked. Remember to stay in line with your topic and
that you will use this literature review to guide your proposal;
especially the needs statement and background.
IX. Revise, Edit, and Proofread
A.
Check your thesis statement. Does it clearly articulate all the
points you’ve covered in your review? Are any points
mentioned that aren’t covered in your review?
B.
Check your body paragraphs against your thesis (25 page
review). Are they related to your thesis? Are they analytical?
Do you discuss the research literature, the methodological
literature, do you have a strong background and problem
statement?

C.
Check the details of your body paragraphs. Do you have
enough support for your topic sentence? Are all the details in
each body paragraph directly related to their respective topic
sentence? Are the points you’re making arranged in such a way
that your reader can clearly follow your line of thinking? Do
you have too much outside support (so much so that it
overwhelms your voice)?
D.
Read your paper carefully (out loud is suggested).
E.
Check your compliance with APA format. Review in-text
documentation and works cited page (using APA Handbook to
do so). Check for any missing citations; fix if necessary.
F. Papers are to be reviewed by students support services
before submitted and you must upload them to Bb and turnitin
for a plagiarism check.
X. Submit the final draft of your paper by the deadline stated.
Examples of Proper Literature Reviews
Example #1: A Brief Section of a Literature Review (Lin and
Dembo 2008)
The first example presented is from a research article (Lin and
Dembo 2008:35) that sought to explain why some juveniles use
illegal drugs and others do not. One of the theories being used
by the authors is social control theory. The following section is
part of the literature review that discusses previous research
findings on the role of this theory in predicting juvenile drug
use.
Hirschi’s (1969) social control theory argued that adolescents
who had no strong bond to conventional social institutions were

more likely to commit delinquency. Many empirical studies that
follow Hirschi’s theory found general support that juveniles
who have strong social bonds are engaged in fewer delinquent
acts (Agnew 1985; Costello and Vowell 1999; Erickson,
Crosboe, and Dornbush 2000; Hindelang 1973; Hirschi 1969;
Junger-Tas 1992; Sampson and Laub 1993; Thornberry et al.
1991). Some studies that specially employed social control
theory to explain juvenile drug use have also found support for
this theory (Ellickson et al. 1999; Krohn et al. 1983; Marcos et
al. 1986; Wiatrowski, Griswold, and Roberts 1981). By
reviewing these studies, one can find that during the adolescent
period (12-17), family and school play influential roles in
influencing youngsters’ behavior. Whereas a defective family
bond increases the probability of youthful drug use or juvenile
delinquency (Denton and Kampfe 1994; Wells and Rankin 1991;
Rankin and Kern 1994; Radosevich et al. 1980), students who
have a weak school bond also have a higher risk of drug use
(Ahlgren et al. 1982; Bauman 1984; Radosevich et al. 1980; Tec
1972).
Notice especially the following: (1) the thorough overview of
previous research, (2) the large number of previous research
studies referenced, (3) the succinct and well-organized writing
style, and (4) the manner in which previous studies are cited.
Also note the following formatting guidelines:
1) If name of author(s) is in the text, put DATE OF
PUBLICATION in parentheses.
2) If one author, use NAME and DATE OF PUBLICATION (and
no punctuation between them).
3) If two authors, use NAME and NAME and DATE OF
PUBLICATION.

4) If three authors, use NAME, NAME, and NAME and DATE
OF PUBLICATION.
5) If four or more authors, use NAME et al. and DATE OF
PUBLICATION.
6) If two or more citations are listed together, order them
alphabetically by first author’s last name.
Example #2: A Brief Section of a Literature Review (Rogoeczi
2008)
The second example is from a research article (Rogoeczi 2008)
that examines whether living in crowded conditions has the
same or a different effect on women and men. The following
section is part of the literature review that discusses previous
research findings on the effect of lack of space in a room on
aggressive actions by women and men. Note that the section
comments on the fact that not all previous research is
consistent. This sometimes is the case and is important to note.
Experimental research varying room size reveals a relatively
consistent pattern of gender differences, with more aggressive
responses to limited space found among males than those
observed among women (Baum and Koman 1976; Epstein and
Karlin 1975; Freedman et al. 1972; Mackintosh, Saegert, and
West 1975; Stokols et al. 1973). Studies examining the effects
of density on children also report sex differences in response to
density, with boys displaying heightened aggression (Loo 1972,
1978). Research on gender differences in withdrawal has
produced more mixed findings (e.g., Loo 1978). Still other
research finds no evidence of sex differences in discomfort as a
result of crowding (Aiello, Epstein, and Karlin 1975; Baum and
Valins 1977) or in the impact of crowding (Evans et al. 2000).
Several longitudinal studies of the impact of household
crowding on psychological distress among college students

reveal no differential effect by gender (Evans and Lepore 1993;
Lepore, Evans, and Schneider 1991). However Karlin, Epstein,
and Aiello (1978) report more physical and psychological
effects among crowded women than men.
Once again, notice the following: (1) the thorough overview of
style, and (4) the manner in which previous studies are cited.
Example #3: An Extended Section of a Literature Review
(Durkin, Wolfe, and Clark 2005).
As an example of an extended section of a literature review, an
article by Keith Durkin, Timothy Wolfe, and Gregory Clark
(2005: 256-261) in Sociological Spectrum is used. The research
examines the ability of social learning theory to explain binge
drinking by college students. Tim Wolfe is chair of the
Education department at Mount Saint Mary’s University and a
Education graduate of Roanoke College.
Introduction
Research Purpose
The abuse of alcohol by college students has been the focus of
considerable concern for several decades. However, one specific
pattern of alcohol consumption, known as binge drinking, has
recently received a tremendous amount of attention from the
media, college personnel, healthcare professionals and
researchers in the behavioral sciences. Binge drinking involves
the consumption of large quantities of alcohol in a single
drinking episode. A number of researchers have operationally
defined binge drinking as the consumption of five or more
alcoholic drinks in a single setting (Alva 1998; Borsari and
Carey 1999; Haines and Spear 1996; Hensley 2001; Ichiyama
and Kruse 1998; Jones et al. 2001; Meilman, Leichliter, and

Presley 1999; Nezlek, Pilkington, and Bilbro 1994; Page,
Scanlan, and Gilbert 1999; Shulenberg et al. 1996). Research
has indicated that this behavior is a prevalent phenomenon on
college campuses nationwide. For instance, a 1993 survey of
17,592 students from 140 colleges and universities, which was
conducted by the Harvard School of Public Health, found that
44% of students reported they had engaged in binge drinking
during the previous two weeks (Weschler et al. 1994).
Subsequent studies conducted in 1997, 1999, and 2001 produced
nearly identical results (Weschler et al. 2002).
There is a growing consensus that binge drinking constitutes a
very serious threat to the well being of many of today’s college
students. In fact, binge drinking has been characterized as the
foremost public health hazard facing college students (Weschler
et al. 1995). Research has indicated that compared to other
college students, binge drinkers are more likely to experience
negative consequences as a result of consuming alcoholic
beverages. These include blackouts, hangovers, missing class
because of drinking, falling behind in their studies, doing
something that they later regretted, arguing with friends, getting
involved in physical fights, and getting into trouble with the
police (Weschler et al. 1994; Weschler et al. 2000). The most
recent research suggests that many of these aforementioned
negative consequences are on the rise nationally (Weschler et
al. 2002). Binge drinking is also related to engaging in high-risk
sexual behaviors, thus putting these students in danger of
contracting sexually transmitted diseases or having an
unplanned pregnancy (Ichiyama and Kruse 1998; Meilman 1993;
Smith and Brown 1998). Moreover, recent research has found
that students who report getting drunk frequently have
significantly higher odds of being victims of assault than their
peers (Hensley 2001; Mustaine and Tewsbury 2000).
Furthermore, it is estimated that more than half of the young
adults who binge drink on a daily basis exhibit indicators of
alcohol abuse or dependency (Shulenberg et al. 1996). Finally,

the tragic alcohol-related deaths of students at several colleges
and universities highlight the potentially fatal consequences of
this activity (Jones et al. 2001; Vicary and Karshin 2002).
Research has further revealed that the negative consequences of
binge drinking are not limited to the students who participate in
this behavior. This activity also has an adverse impact on other
members of the university community. The concepts of
“secondary binge effects” (Weschler et al. 1994; Weschler et al.
1995) and “secondhand effects” (Weschler et al. 2002) have
emerged in the literature to describe the problems that are the
direct result of other students’ binge drinking. Some of these
secondary binge effects include being verbally insulted or
abused, being physically assaulted, having one’s property
damaged, experiencing unwelcome sexual advances, and having
sleep or studying disturbed because of the conduct of
intoxicated students. The recent alcohol-related riots on a
number of campuses and neighboring communities are also
examples of these secondary consequences (Vicary and Karshin
2002). Neighbors living near campuses frequently report a
lower quality of life as a result of student binge drinking
because of noise disturbances, litter, drunkenness, vandalism,
vomiting, and urination (Weschler 2002).
Although a number of recent studies have sought to identify
factors that are associated with binge drinking by college
students (Alva 1998; Ichiyama and Kruse 1998; Page et al.
1999; Turrisi 1999; Weschler, et al. 1995), research that applies
the various sociological perspectives, particularly theories of
deviant behavior to this phenomenon is particularly limited. For
instance, Durkin, Wolfe, and Clark (1999) applied social bond
theory to the binge drinking behavior of undergraduate students
at one private college. Also, Workman (2001) conducted an
ethnographic study at one university to examine the social
construction and communication of norms about excessive
drinking among fraternity members. The relative absence of

sociological research on binge drinking is an extremely
significant oversight. Given the fact that sociological theories
of deviance typically have a strong explanatory value, the
current undertaking can make an important contribution to
understanding this problematic behavior. The purpose of the
current undertaking is to apply one of the leading sociological
explanations of deviant behavior, social learning theory (Akers
1985, 2000), to binge drinking by college students.
Once again, notice the following: (1) the thorough overview of
style, and (4) the manner in which previous studies are cited. In
addition, notice how the authors use the last paragraph to
explain the need for a sociological study of binge drinking.
References
Durkin, Keith F., Timothy W. Wolfe, and Gregory A. Clark.
2005. “College Students and Binge Drinking: An Evaluation of
Social Learning Theory.” Sociological Spectrum 25(3): 255-
272.
Lin, Wen-Hsu and Richard Dembo. 2008. “An Integrated Model
of Juvenile Drug Use: A Cross-Demographic Groups Study.”
Western Criminology Review 9(2): 33-51.
Regoeczi, Wendy C. 2008. “Crowding in Context: An
Examination of the Differential Responses of Men and Women
to High-Density Living Environments.” Journal of Health and
Social Behavior 49(5): 254-268.
Additional Helpful References
Galvan, Jose L. 2009. Writing Literature Reviews: A Guide for
Students of the Social and Behavioral Sciences. 4th ed.

Glendale, CA: Pyrczak Publishing. (This source provides a
detailed step-by-step process of conducting and writing a
literature review.)
Machi, Lawrence A. and Brenda T. McEvoy. 2008. The
Literature Review: Six Steps to Success. Thousand Oaks, CA:
Corwin Press.
Page 3 of 12
Name:
Statement
of Focus (100 points)
.
1. What area of ESE or Education do you feel YOU can change
or improve? Please think of this in light of your proposed action
research focus this term.
I would like to focus on increasing on-task behavior during
distance learning time in gifted students diagnosed with ADHD
at elementary level.
2. Why is this change particularly meaningful to YOU as an
educator?
This change is particularly meaningful to me because, as an
educator, I want my students to successfully engaged in
academic learning time outside of the classroom setting.
3. What do other educators or professionals tell you when YOU
discuss this topic with them?

Other educators agree that the classroom setting is the most
successful one when it comes to knowledge acquisition because
in this setting, students have less distractions than at home.
Another concern that educators have in relation to this matter is
that at home setting there is no scholar schedule and/or
structure as in schools and also caregivers are not trained on
teaching skills and most of the time responses to exercises/test
can be biased by their help and/or other distractors environment
related.
4. How is the desired outcome a part of YOUR educational
philosophy?
The School is the ideal setting for learning acquisition for
gifted students, but they can also learn in home setting if they
have the appropriate resources. Applying behavioral
intervention programs to keep them focused and engaged on
tasks can be a method to successfully increase their academic
learning time.
5. Describe the situation with your student/group of students
that you want to change by implicitly focusing on: (What is the
problem you would like to improve)
Who? What? When? Where? How?
I would like to increase the on-task behavior during distance
learning time for gifted students at elementary level, at home
setting. I will apply a behavioral intervention plan, based on the
results of a preference assessment previously done according to
functions of the behaviors observed.
International Review of Research in Open and Distributed
Learning

Volume 18, Number 2
April – 2017
Analysis of Time-on-Task, Behavior Experiences, and
Performance in Two Online Courses with Different
Authentic Learning Tasks
Sanghoon Park
University of South Florida
Abstract
This paper reports the findings of a comparative analysis of
online learner behavioral interactions, time-
on-task, attendance, and performance at different points
throughout a semester (beginning, during, and
end) based on two online courses: one course offering authentic
discussion-based learning activities and
the other course offering authentic design/development-based
learning activities. Web log data were
collected to determine the number of learner behavioral
interactions with the Moodle learning management
system (LMS), the number of behavioral interactions with peers,
the time-on-task for weekly tasks, and the

recorded attendance. Student performance on weekly tasks was
also collected from the course data.
Behavioral interactions with the Moodle LMS included resource
viewing activities and
uploading/downloading file activities. Behavioral interactions
with peers included discussion postings,
discussion responses, and discussion viewing activities. A series
of Mann-Whitney tests were conducted to
compare the two types of behavioral interactions between the
two courses. Additionally, each student's
behavioral interactions were visually presented to show the
pattern of their interactions. The results
indicated that, at the beginning of the semester, students who
were involved in authentic
design/development-based learning activities showed a
significantly higher number of behavioral
interactions with the Moodle LMS than did students involved in
authentic discussion-based learning
activities. However, in the middle of the semester, students
engaged in authentic discussion-based learning
activities showed a significantly higher number of behavioral
interactions with peers than did students
involved in authentic design/development-based learning
activities. Additionally, students who were given

authentic design/development-based learning activities received
higher performance scores both during
the semester and at the end of the semester and they showed
overall higher performance scores than
Performance in Two Online Courses with Different Authentic
Learning Tasks
Park
214
students who were given authentic discussion-based learning
activities. No differences were found between
the two groups with respect to time-on-task or attendance.
Keywords: authentic learning task, behavioral experience,
online learning, Web log data, time-on-task
Introduction
The number of online courses has been growing rapidly across
the nation in both K-12 and higher education.
According to the U.S. Department of Education’s National
Center for Education Statistics (NCES),
approximately half of all K-12 school districts nationwide
(55%) had students enrolled in at least one online

course (National Center for Education Statistics [NCES], 2011).
In higher education, more than 7.1 million
students are taking at least one online course (Allen & Seaman,
2014). These numbers are projected to grow
exponentially as more universities are striving to meet the
increasing demand for online courses. Online
courses are expected to provide formal learning opportunities at
the higher education level using various
learning management platforms (Moller, Foshay, & Huett, 2008;
Shea & Bidjerano, 2014; Wallace, 2010).
Consequently, E-learning systems, or learning management
systems (LMSs), are being advanced to provide
students with high quality learning experiences and high quality
educational services in their online courses
(Mahajan, Sodhi, & Mahajan, 2016).
Although the quality of an online learning experience can be
defined and interpreted differently by the
various stakeholders involved, previous studies identified both
time flexibility and authentic learning tasks
as two key factors affecting successful online learning. Time
flexibility has been regarded as the most
appealing option for online learning (Romero & Barberà, 2011)
as it allows online learners to determine the

duration, pace, and synchronicity of the learning activities
(Arneberg et al., 2007; Collis, Vingerhoets, &
Moonen, 1997; Van den Brande, 1994). Recently, Romero and
Barberà (2011) divided time flexibility into
two constructs, instructional time and learner time, and asserted
the need for studies that consider the time
attributes of learners, such as time-on-task quality. Authentic
tasks form the other aspect of successful
online learning. Based on the constructivist learning model,
online students learn more effectively when
they are engaged in learning tasks that are relevant and/or
authentic to them (Herrington, Oliver, & Reeves,
2006). Such tasks help learners develop authentic learning
experiences through activities that emulate real-
life problems and take place in an authentic learning
environment (Roblyer & Edwards, 2000). Authentic
learning activities can take many different forms and have been
shown to provide many benefits for online
learners (Lebow & Wager, 1994). For example, authentic tasks
offer the opportunity to examine the task
from different perspectives using a variety of available online
resources. Additionally, authentic tasks can
be integrated across different subject areas to encourage diverse
roles and engage expertise from various

interdisciplinary perspectives (Herrington et al., 2006). To
maximize the benefits of authentic tasks,
Herrington, Oliver, and Reeves (2006) suggested a design model
that involves three elements of authentic
learning: tasks, learners, and technologies. After exploring the
respective roles of the learner, the task and
the technology, they concluded that synergy among these
elements is a strong contributor to the success of
online learning. Therefore, online learning must be designed to
incorporate authentic learning tasks that
are facilitated by, and can be completed using, multiple types of
technologies (Parker, Maor, & Herrington,
2013).
Learning Tasks
Park
215
In summary, both time flexibility and authentic learning tasks
are important aspects of a successful online
learning experience. However, few studies have investigated
how online students show different behavioral

interactions during time-on-tasks with different authentic
learning tasks, although higher levels of online
activity were found to be always associated with better final
grades greater student satisfaction (Cho & Kim,
2013). Therefore, the purpose of this study was to compare
behavioral interactions, time-on-task,
attendance, and performance between two online courses
employing different types of authentic tasks.
Web Log Data Analysis
Web log data analysis or Web usage analysis is one of the most
commonly used methods to analyze online
behaviors using electronic records of a system-user interactions
(Taksa, Spink, & Jansen, 2009). Web logs
are the collection of digital traces that provide valuable
information about each individual learner’s actions
in an online course (Mahajan et al., 2016). Many recent LMSs,
such as CANVAS, or the newly upgraded
LMSs, such as Blackboard or Moodle, offer various sets of Web
log data in the form of learning analytics.
The data usually contain course log history, number of views
for each page, number of comments,
punctuality of assignment submission, and other technology
usage. Web log files also contain a list of user
actions that occurred during a certain period of time (Grace,

Maheswari, & Nagamalai, 2011). This vast
amount of data allows instructors and researchers to find
valuable information about learners’ online
course behaviors, such as how many times per day and how
often students log in, how many times and how
often they post to discussion boards, how many students submit
assignments on time, how much time they
spend on each learning task, etc. Web log data also provides
personal information about online learners,
such as each student’s profile and achievement scores, and each
student’s behavioral interaction data, such
as content viewing, discussion posting, assignment submission,
writing, test taking, task performances, and
communications with peers/instructor (Mostow et al., 2005).
The data can be presented in the form of
visualization to support students and instructors in the
understanding of their learning/teaching
experiences. Therefore, the Web log analysis method offers a
promising approach to better understand the
behavioral interactions of online learners at different points
during the semester. Researchers can use Web
log data to describe or make inferences about learning events
without intruding the learning event or
involving direct elicitation of data from online learners (Jansen,

Taksa, & Spink, 2009). Although Web log
data is a source of valuable information to understand online
behaviors, it also has to be noted that
researchers must be careful when interpreting the data with a
fair amount of caution because Web log data
could be misleading. For example, an online student might
appear to be online for a longer time than she/he
actually participated in a learning activity. Therefore, prior to
conducting the Web log analysis, a researcher
needs to understand the type of behavioral data to be analyzed
based on the research questions and
articulates the situational and contextual factors of the log data.
Using the timestamps showing when the
Web log was recorded, the researcher can make the observation
of behaviors at certain point of time and
decide the validity of the online behavior (Jansen et al., 2009).
Behavioral Interactions
Previous studies have shown the benefits of analyzing Web log
data to understand the online learning
behaviors of students. Hellwege, Gleadow, and McNaught
(1996) conducted a study of the behavioral
patterns of online learners while studying a geology Web site
and reported that learners show a pattern of

accessing the most recent lecture notes prior to accessing the
Web site materials. Sheard, Albrecht, and
Butbul (2005) analyzed Web log files and found that knowing
when students access various resources helps
Learning Tasks
Park
216
instructors understand the students’ preferred learning patterns.
While analyzing log data to investigate
learning effectiveness, Peled and Rashty (1999) found that the
most popular online activities were, in
general, passive activities, such as retrieving information, rather
than contributing activities. Dringus and
Ellis (2005) reported on how to analyze asynchronous
discussion form usage data to evaluate the progress
of a threaded discussion. Several recent studies showed a
positive link between students' online activities
and their final course grades. For example, Valsamidis and
Democritus (2011) examined the relationship
between student activity level and student grades in an e-
learning environment and found that the quality

of learning content is closely related to student grades. Also,
Dawson, McWilliam, and Tan (2008) found
that when students spend more time in online activities and
course discussions, they earned higher final
grades. Similarly, Macfadyen and Dawson (2010) reported that
the numbers of messages postings, email
correspondences, and completed assessments were positively
correlated with students' final course grades.
Most recently, Wei, Peng, and Chou's study (2015) showed the
positive correlations between the number of
discussion postings, reading material viewings, and logins with
students' final exam scores. Although the
previous studies utilized Web log data to investigate the
relationships between students' behavioral
interactions and learning achievement, few studies examined
how online students' behavioral interactions
are different at different phases of online learning when
involved in two types of authentic learning tasks.
Online Learning Experience
The overall online learning experience consists of continuous
behavioral interactions that are generated
while completing a series of learning tasks (Park, 2015).
Therefore, an examination of the nature of the

learning tasks and the influences of the learning tasks on
student behaviors is needed. The examined short-
term learning experiences can then be combined to create a big
picture of the online learning experience
within a course. According to Veletsianos and Doering (2010),
the experience of online learners must be
studied throughout the semester due to the long-term nature of
online learning programs. To analyze the
pattern of behavioral interactions, this study employed time and
tasks as two analysis frames because both
time and tasks form essential dimensions of a behavioral
experience, as shown in Figure 1. An online
learning experience begins at the starting point (first day of the
course) and ends at the ending point (last
day of the course). In between those two points, a series of
learning tasks are presented to provide learners
with diverse learning experiences. As the course continues, the
learner continues to interact with learning
tasks and eventually accumulates learning experiences by
completing the learning tasks (Park, 2016).
Students learning experiences are built up from the previous
learning tasks because learning tasks are not
separated from each other as shown in the spiral area in Figure
1. Hence, to analyze behavioral interactions

in online learning, both the type of learning tasks and the time-
on-task must be analyzed simultaneously.
In this paper, the researcher gathered and utilized Web log data
to visualize the behavioral interaction
patterns of online learners during the course of a semester and
to compare the behavioral interactions
between two online courses requiring different types of learning
tasks.
Learning Tasks
Park
217
Figure 1. Online learning experience - time and tasks.
Research Questions
1. Do online learners' behavioral interactions with Moodle LMS
differ between a course employing
authentic discussion-based learning tasks and a course
employing authentic design/development-
based learning tasks?

2. Do online learners' behavioral interactions with peers differ
between a course employing authentic
discussion-based learning tasks and a course employing
authentic design/development-based
learning tasks?
3. Does online learners' time-on-task differ between a course
employing authentic discussion-based
learning tasks and a course employing authentic
design/development-based learning tasks?
4. Does online learners' attendance differ between a course
employing authentic discussion-based
learning tasks and a course employing authentic
5. Does online learners' academic performance differ between a
course employing authentic
authentic design/development-based
learning tasks?
Method
Setting
In this study, the researcher purposefully selected two online
courses as units of analysis. The two courses

were purposefully selected because of the different learning
approach that each course employed to design
authentic learning activities and the extent to which technology
was used. Course A activities were designed
based on the constructivist learning approach while Course B
activities were designed based on the
constructionist learning approach. Both the constructionist
approach and constructivist approach hold the
basic assumption that students build knowledge of their own
and continuously reconstruct it through
personal experiences with their surrounding external realities.
However, the constructionist approach is
Learning Tasks
Park
218
different from the constructivist approach in that constructionist
learning begins with a view of learning as
a construction of knowledge through constructing tangible
projects or creating digital artifacts (Kafai, 2006;
Papert, 1991). The title of Course A was Program Evaluation, in

which the major course activities consisted
of textbook reading, weekly online discussions, and a final
evaluation plan proposal. Students enrolled in
this course were expected to read the textbook, participate in
weekly discussion activities, and complete a
program evaluation plan. Course B was titled Instructional
Multimedia Design/Development, which
consisted of a series of hands-on tasks to design and develop
multimedia materials using different
multimedia authoring programs. Students were required to
review related literature and tutorials on
multimedia design during the semester and to create audio-
based, visual-based, and Web-based
multimedia materials through a series of hands-on-activities.
The comparison of course requirements, key
learning activities, authentic tasks, and technology use between
the two online courses is presented in Table
1.
Table 1
Comparison of Course Requirements, Key Learning Activities,
Authentic Tasks, and Technology use
Between Courses

Course A Course B
Course* requirements Textbook reading & online discussion
Multimedia design/development
Key learning activities
nline
discussion
online discussion
design/development
design/development
rsonal Website development
module design/development
world scenario and presented with

contextualized data for weekly
discussions.
-defined
and open to multiple
interpretations.
each discussion topic.
variety of related documents and
Web resources.
o create a
course outcome (program
evaluation plan proposal) that
could be used in their own
organization.
create instructional multimedia
materials to solve a performance
problem that they identified in their
own fields.
scope of each multimedia project to
solve the unique performance
problems they had identified.
different multimedia programs and
apply various design principles that
were related to their own projects.

Web based learning module that
can be used as an intervention to
solve the identified performance
problem in their own organizations.
Technology use Students utilized the following
technology to share their ideas and
insights via weekly discussions
Students utilized the following
technology to design and create
instructional multimedia materials
Learning Tasks
Park
219
based on the constructivist learning
approach:
-Word

-PowerPoint
based on the constructionist
learning approach:
design tools
Note.
* A course in this study refers to a general online class that
delivers a series of lessons and learning tasks
(online lectures, readings, assignments, quizzes, design and
development activities, etc.).
** Authentic tasks were designed based on 10 characteristics of
authentic activities/tasks defined by
Herrington et al. (2006).
Both courses were delivered via Moodle LMS and met the
Quality Matters (QM) standards. Moodle is an

open-source LMS that helps educators create online learning
courses. It has been used as a popular
alternative to proprietary commercial online learning solutions
and is distributed free of charge under open
source licensing (Romero, Ventura, & Garcia, 2008). QM
specifies a standard set employed to certify the
quality of an online course (www.qualitymatters.org). Both
courses A and B in this study met the required
standards for high quality online course design after a rigorous
review process by two certified QM
reviewers.
Participants
As two courses with different online learning tasks were
purposefully selected, 22 graduate students who
were enrolled in two 8 week long online courses participated in
this study. Twelve students were enrolled
in Course A, and 10 students were enrolled in Course B.
Excluding four students, two who dropped from
each course due to personal reasons, the data reported in this
paper concern 18 participants, 10 students (4
male and 6 female) in Course A and 8 students (all female) in
Course B, with a mean age of 32.60 years (SD
= 5.76) and 35.25 years (SD = 9.66), respectively. The average

number of online courses the study
participants had taken previously was 11.40 (SD = 4.88) for
Course A and 11.38 (SD = 12.28) for Course B,
indicating no significant difference between the two courses.
However, it should be noted that the number
of students who had not previously taken more than 10 online
courses was higher in Course B (five
participants) than in Course A (three participants). Fifteen of
the 18 participants were teachers: five taught
elementary school, five taught middle school, and five taught
high school. Of the remaining three
participants, one was an administrative assistant, one was a
curriculum director, and one was an
instructional designer.
Learning Tasks
Park
220
Figure 2. Example of Web log data screen.

Data
In this study, the researcher examined behavioral interactions
by utilizing students’ Web log data acquired
from Moodle LMS used in this study (Figure 2). The obtained
sets of data were significant for this study
because they contained timestamp-sequenced interaction
activities that are automatically recorded for each
student with pre-determined activity categories such as view
discussion, post discussion, view resources,
etc. Hence, it clearly showed the type of activities each student
followed in order to complete a given online
learning task. The researcher also ensured the accuracy of data
by following the process to decide the
validity of the online behavior (Jansen et al., 2009). First, the
researcher clearly defined the type of
behavioral data to be analyzed based on the research questions
(Table 2), and second, the researcher
articulated the situational and contextual factors of the log data
by cross-examining the given online tasks
and recorded students activities. Lastly, the researcher checked
the timestamps for each activity to confirm

Learning Tasks
Park
221
the time and the length of data recorded. The data were then
converted to Excel file format and computed
based on three semester phases for further analysis. These
phases were phase 1 ─ beginning of the semester,
phase 2 ─ during the semester, and phase 3 ─ end of the
semester. An example of a Web log data screen is
presented in Figure 2. The data show online learner behaviors in
chronological order. Based on the type of
behavioral activities, the researcher identified two categories of
behavioral interactions that affect task
completion: interactions with the Moodle LMS and interactions
with peers. Table 2 presents the two
categories of behavior interactions and example behaviors for
each category.
Table 2
Categories of Behavioral Interactions and Description
Categories of behavioral
interaction
Behavior description

(Operational definition)
Interactions with Moodle
LMS
(# of times quiz participation - quiz completion and submission)
(# of visits to the Resource page)
(# of visits to files page - file uploading and file downloadng)
Interactions with peers
ion viewing
(# of times discussion viewed)
(# of times discussion posted - making initial posts)
(# of times discussion responded - making comments or replies)
Among the identified behaviors, quiz taking was excluded from
the analysis because it was a behavioral
interaction that only applied to Course A. Student attendance
and time-on-task were collected from
recorded attendance data and each student’s weekly reported
time-on-task. Weekly performance scores

were also collected from the course instructors and from the
students with student permission. Due to the
different grading systems, task scores from the two courses
were converted to a 1 (minimum) to 100
(maximum) scale and combined based on the corresponding
weeks for each phase.
Results
Collected data were analyzed to answer each of the five
research questions. Table 3 displays the descriptive
statistics of behavioral interactions with the Moodle LMS,
behavioral interactions with peers, time-on-task,
attendance, and performance for each of the two courses.
A series of Mann-Whitney tests (Field, 2013), the non-
parametric equivalent of the independent samples t-
test, were used to compare the two types of behavioral
interactions, time-on-task, attendance, and
performance between the two courses. The Mann-Whitney test
was selected for use in this study because
the data did not meet the requirements for a parametric test, and
the Mann-Whitney test has the advantage
of being used for small samples of subjects, (i.e., between five
and 20 participants) (Nachar, 2008).

Learning Tasks
Park
222
RQ1: Do online learners' behavioral interactions with the
Moodle LMS differ between
a course employing authentic discussion-based learning tasks
and a course employing
authentic design/development-based learning tasks?
The average number of behavioral interactions with the Moodle
LMS between the two courses was
compared using the Mann-Whitney test. Among the three phases
compared, the average number of Moodle
LMS interactions in phase 1 (weeks 2/3) was significantly
different, as revealed in Figure 3. In phase 1, the
average number of Moodle LMS interactions in Course B (M =
32.00, Mdn = 31.50) was significantly higher
than the average number of Moodle LMS interactions in Course
A (M = 19.60, Mdn = 20.00), U = 12.00, z
= - 2.50, p < .05, r = -.59, thus revealing a large effect size
(Field, 2013). In phases 2 and 3, however, the
average number of Moodle LMS interactions were not
significantly different between the two courses.

Nonetheless, the total number of Moodle LMS interactions
between the two courses was significantly
different as the total number of Moodle LMS interactions in
Course B (M = 74.13, Mdn = 73.50) was
significantly higher than the average number of Moodle LMS
interactions in Course A (M = 59.70, Mdn =
65.50), U = 17.50, z = - 2.01, p < .05, r = -.47, indicating a
medium to large effect size.
Figure 3. Average number of behavioral interactions with the
Moodle LMS for each phase of the semester
for two courses.
0
5
10
15
20
25
30

35
Phase1 Phase2 Phase3
Course A
Course B
Learning Tasks
Park
223
Table 3
Descriptive Statistics of Behavioral Interactions, Time,
Attendance, and Performance
Phase 1 (Weeks 2/3) Phase 2 (Weeks 4/5/6) Phase 3 (Weeks
7/8) All three phases
Course A
(n = 10)
Course B
(n = 8)
Course A
(n = 10)

Course B
(n = 8)
Course A
(n = 10)
Course B
(n = 8)
Course A
(n = 10)
Course B
(n = 8)
M SD M SD M SD M SD M SD M SD M SD M
SD
Behavioral
interactions a
LMS
interactions
19.60(5.36) 32.00(9.37) 23.10(6.72) 19.63(7.15) 17.00(5.77)
22.50(12.09) 59.70(10.46) 74.13(19.28)
Peer
interactions
64.50(24.04) 86.75(42.60) 126.30(57.88) 58.25(25.39)
65.70(41.17) 48.25(22.38) 256.50(99.69) 193.25(69.77)
Attendance b 9.90(2.99) 10.75(2.82) 15.20(3.05) 13.50(4.47)

11.70(2.21) 9.75(4.20) 36.80(7.57) 34.00(9.15)
Time-on-task c 375.00(53.59
)
700.63(549.52
)
564.00(155.27
)
1919.38(1928.10
)
252.50(140.34
)
360.00(304.2
6)
1191.50(314.80
)
2980.00(2713.6
5)
Performance
Task score d 185.43(11.02) 195.00(4.47) 241.25(29.71)
283.59(21.37) 79.00(15.23) 96.43(1.66) 505.68(46.31)
575.02(26.03)
Note.
a Average number of interactions per phase
b Average number of course participations per phase (Logins)
c Time presented in minutes
d Scores ranging from 0 (minimum) to 200 (maximum) in phase
1, from 0 (minimum) to 300 (maximum) in phase 2, from 0

(minimum) to 100
(maximum) in phase 3
Learning Tasks
Park
224
RQ2: Do online learners' behavioral interactions with peers
differ between a course
employing authentic discussion-based learning tasks and a
course employing
authentic design/development-based learning tasks?
The average number of behavioral interactions with peers for
the two courses was compared using the
Mann-Whitney test. Among the three phases, the average
number of interactions with peers in phase 2
(weeks 4/5/6) was significantly different, as evidenced in Figure
4. In phase 2, the average number of peer
interactions in Course A (M = 126.30, Mdn = 111.50) was
significantly higher than the average number of
peer interactions in Course B (M = 58.25, Mdn = 59.50), U =
7.00, z = - 2.93, p < .01, r = -.69, thus revealing
a large effect size. However, the average number of peer

interactions was not significantly different between
the two courses in phases 1 and 3, nor was the total number of
peer interactions between the two courses
significant.
Figure 4. Average number of behavioral interactions with peers
for each phase of the semester for the two
courses.
The findings for both research questions 1 and 2 show the
statistical comparisons of Moodle LMS
interactions and peer interactions between two online courses
involving different types of authentic
learning tasks. To help understand the exact type of behavioral
interactions and possible patterns, the
researcher visualized each student's behavioral interaction
pattern, as shown in Figures 5, 6, and 7. Each
category of students' behavioral interactions was imported into
an Excel spreadsheet with different color
themes (Figure 5).
0
20

40
60
80
100
120
140
Course A
Course B
Learning Tasks
Park
225
Figure 5. Legend of color themes.
Student 1

Student 2
Student 3
Student 4
Student 5
Student 6
Student 7
Student 8
Student 9
Student 10
Figure 6. Behavioral interaction pattern for each individual
student in Course A.
Learning Tasks
Park
226

Student 1
Student 2
Student 3
Student 4
Student 5
Student 6
Student 7
Student 8
Figure 7. Behavioral interaction pattern for each individual
student in Course B.
Blue colors represent a student's course exploration activities,
such as course viewing and other user
viewing. Brown colors represent a student's interactions with
the Moodle LMS, and green colors represent
a student's interactions with peers. Each square in the pattern
graph represents one occurrence of the case.
Each pattern line represents the total behavioral interactions
that occurred in each week. Through visual
representations of behavioral interactions, different patterns
were identified in the two courses. Most of the

students in course A showed a ( ) shape of behavioral patterns,
while students in Course B
showed a ( ) shape of behavioral patterns. In other words,
students in Course A tend to
show more behavior interactions as they move toward the end of
the semester, while students in course B
showed higher behavioral interactions in the first week of the
semester and also at the end of the semester.
RQ3: Does online learners' time-on-task differ between a course
employing authentic
authentic
Time-on-task for weekly authentic tasks for the two courses was
compared using the Mann-Whitney test.
No significant differences were found in any of the three phases
or for the entire semester (Figure 8).
Learning Tasks
Park
227

Figure 8. Average time-on-task (in minutes) for each phase for
the two courses.
RQ4: Does online learners' attendance differ between a course
employing authentic
authentic
Attendance for weekly authentic tasks for the two courses was
compared using the Mann-Whitney test. No
significant differences were found in any of the three phases or
for the entire semester (Figure 9).
Figure 9. Average attendance for each phase for the two
courses.
RQ5: Does online learners' academic performance differ
between a course employing
authentic discussion-based learning tasks and a course
employing authentic
The average task score between the two courses was compared
using the Mann-Whitney test. Among the
three phases compared, the average scores in phases 2 and 3
were significantly different, as displayed in
Figure 10. In phase 2, the average score in Course B (M =
283.59, Mdn = 290.00) was significantly higher

than the average score in Course A (M = 241.25, Mdn =
240.47), U = 9.00, z = -2.76, p < .01, r = -.65,
revealing a large effect size. In phase 3, the average score in
Course B (M = 96.43, Mdn = 96.43) was
significantly higher than the average score in Course A (M =
79.00, Mdn = 85.00), U = 7.50, z = -2.94, p
< .01, r = -.69, indicating a large effect size. However, the task
scores were not significantly different in
0
500
1000
1500
2000
2500
Course A
Course B
0
2
4

6
8
10
12
14
16
Course A
Course B
Learning Tasks
Park
228
phase 1. The total task scores for the entire semester for the two
courses were significantly different. The
total score for Course B (M = 575.02, Mdn = 585.43) was
significantly higher than the total score for Course
A (M = 505.68, Mdn = 495.47), U = 8.00, z = -2.85, p < .01, r =

-.67, indicating a large effect size.
Figure 10. Average score in each phase for the two courses.
In addition to the Mann-Whitney test comparisons, a Spearman's
rank-order correlation was also run to
determine the relationship between all 18 students' behavioral
interactions, time-on-task, and performance
per week.
Table 4
Significant Correlations between Behavioral Interactions, Time-
on-Task, and Performance
Weeks Correlation rs(16) p value *
Week2 Discussion viewing - Discussion response .794 .000
Week3 Discussion viewing - Discussion response .639 .004
Week4 Discussion viewing - Discussion posting .742 .000
Resource viewing – Discussion posting .632 .005
Resource viewing – Discussion viewing .631 .005
Week5 Discussion viewing - Discussion posting .599 .009
Resource viewing – Discussion posting .792 .000
Resource viewing – Discussion viewing .703 .001
Week6 File uploading - Score .650 .003
File uploading - Discussion posting .732 .001
Week7/8 File uploading - Discussion posting .622 .006

Discussion viewing - discussion response .661 .003
Note. * All correlations are significant at the 0.01 level (2-
tailed).
Although no overall significant correlations were found between
time-on-task and behavioral interactions,
or between performance scores and behavioral interactions,
there were several noticeable patterns found
among behavioral interactions. For example, during the first
half of the semester, strong positive
correlations were found between discussion reviewing and
discussion response /discussion posting
0
50
100
150
200
250
300
Course A
Course B

Learning Tasks
Park
229
behaviors. Then, during the second half of the semester,
resource viewing, discussion posting, and file
uploading behaviors showed strong positive correlations.
Especially in week 6, students scored higher when
they showed more file uploading behaviors with discussion
postings.
Discussion
Time flexibility and authentic tasks are two factors that affect
the success of an online learning experience.
However, the type of behavioral interactions students exhibit at
different points when they are involved in
different types of authentic tasks is not well understood.
Accordingly, this study attempted to analyze and
visualize the behavioral interactions of online learners at
different times during the semester and compare
the occurrences of these behavioral interactions in two online

courses. The study found that online students
exhibit different behavioral interactions when they are involved
in two different authentic online learning
activities. Students in authentic design/development-based
learning activities demonstrated more
behavioral interactions with the Moodle LMS at the beginning
of the course, whereas students in authentic
discussion-based learning activities exhibited more behavioral
interactions with peers during the middle of
the semester. Overall, attendance and time-on-task did not
differ between the two courses. Understanding
time flexibility as the capacity to spend time-on-task at
different times of the day and week (Romero &
Barberà, 2011), the results indicate that students are likely to be
involved in behavioral interactions with
the Moodle LMS early in the course if given tasks require
authentic design/development learning activities.
This finding could be viewed from the perspective of student
time management. In other words, students
in the design/development course tried to understand the scope
of the design/development projects early
in the semester so they could plan the design/development of
the multimedia materials for the semester.
This notion is supported by their attendance and time-on-task

(Figures 8 and 9). Although students in
Course B did not actively participate in behavioral interactions
with peers in the middle of the semester,
they attended the course regularly and spent significantly more
time working on given tasks compared with
students in Course A. Given the different behavioral interaction
patterns found in the different authentic
online tasks, the findings support the importance of designing
technological learning resources at different
points of the semester depending on the type of authentic
learning tasks and on the needs of the student
(Swan & Shin, 2005).
Another important finding of this study is that the correlations
between student performance and each type
of student behavioral interactions according to Spearman's rank
correlation coefficients were not
significant. The evidence offers the possibility of behavioral
interactions being an intermediate variable,
suggesting that more indicators must be examined to understand
factors affecting student performance in
online learning. In fact, many of the online learning analytics
focus on behavioral indicators rather than on
the psychological aspects of learning, such as cognitive
involvement, academic emotions, and motivation.

Therefore, we must seek ways to incorporate a different
methodology to approach the online learning
experience in a holistic way. For example, the experience
sampling method (ESM) combined with learning
analytics would be a good alternative method to analyze the
multiple dimensions of the online learning
experience.
Learning Tasks
Park
230
Conclusion
This study analyzed the behavioral interactions of online
learners and compared the differences in
behavioral interactions for two online courses each with
different authentic learning tasks. Since the first
course was designed based on a constructivist approach, and the
second course from a constructionist
perspective, the analysis results showed that students in each

course experienced different behavioral
interactions during the semester. The findings imply that when
designing an online course that involves
authentic learning tasks, instructional designers need to
consider optimizing learners' behavioral
interaction sequence to maximize their learning effectiveness.
For example, interactions with peers should
be encouraged when designing an online course based on the
constructive belief (Lowes, Lin, & Kinghorn,
2015). Unlike other previous studies, however, this study did
not find the direct relationship between the
behavioral interactions, whether with Moodle LMS or peers, and
performance scores. Previous studies such
as Davies and Graff's (2005) also reported no relationship
between discussion forum participation and final
course grades. As discussed, behavioral interactions could be an
intermediate variable affected by students'
cognitive involvement and motivation, thus their psychological
online learning experiences also need to be
considered when analyzing students' Web log data. There are
several limitations to this study. First, the
behavioral interaction data collected using Web logs are limited
only to internal data stored in the Moodle
LMS server. External communication data, such as email

correspondences or conference calls, were not
included in the data analysis. Second, although the study was
conducted using two purposefully selected
courses to provide a rich description of the behavioral pattern
for each individual student, future
researchers wanting to make generalizations about the findings
of this study will need to increase the
number of participants. Third, this study only analyzed the
behavioral patterns of online learners, and thus,
there is a need to examine how these behavior patterns are
related to other learning experiences such as a
cognitive processing and affective states. This holistic approach
to understanding learning experiences will
help researchers obtain a more comprehensive picture of the
interactions among the cognitive processes,
affective states, and behavioral patterns. With the meticulous
analysis of the individual learner’s learning
experience, we can gain deeper insight into ways to design the
optimal online learning experience.
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1 3
Eur Child Adolesc Psychiatry (2017) 26:1471–1481
https://doi.org/10.1007/s00787-017-1006-y
ORIGINAL CONTRIBUTION
Time‑on‑task effects in children with and without ADHD:
depletion of executive resources or depletion of motivation?

Tycho J. Dekkers1,2 · Joost A. Agelink van Rentergem1 ·
Alette Koole1 ·
Wery P. M. van den Wildenberg1,3 · Arne Popma2,4,5 · Anika
Bexkens6,7 ·
Reino Stoffelsen2,4 · Anouk Diekmann2,8 · Hilde M.
Huizenga1,3,9
Received: 13 June 2016 / Accepted: 17 May 2017 / Published
online: 23 May 2017
© The Author(s) 2017. This article is an open access
publication
(stop-signal reaction time) and attention (reaction time
variability and errors), was administered in 96 children
(42 ADHD, 54 TD controls; aged 9–13). To differentiate
between depletion of resources and depletion of motivation,
the SST was administered twice. Half of the participants
was reinforced during second task performance, potentially
counteracting depletion of motivation. Multilevel analyses
indicated that children with ADHD were more affected by
time-on-task than controls on two measures of inattention,
but not on inhibition. In the ADHD group, reinforcement
only improved performance on one index of attention (i.e.,
reaction time variability). The current findings suggest that
time-on-task effects in children with ADHD occur spe-
cifically in the attentional domain, and seem to originate
in both depletion of executive resources and depletion of
motivation. Clinical implications for diagnostics, psycho-
education, and intervention are discussed.
Keywords ADHD · Executive functioning · Depletion ·
Time-on-task · Reinforcement · Inhibition
Abstract Children with attention-deficit/hyperactiv-
ity disorder (ADHD) are characterized by deficits in their

executive functioning and motivation. In addition, these
children are characterized by a decline in performance as
time-on-task increases (i.e., time-on-task effects). However,
it is unknown whether these time-on-task effects should be
attributed to deficits in executive functioning or to deficits
in motivation. Some studies in typically developing (TD)
adults indicated that time-on-task effects should be inter-
preted as depletion of executive resources, but other stud-
ies suggested that they represent depletion of motivation.
We, therefore, investigated, in children with and without
ADHD, whether there were time-on-task effects on execu-
tive functions, such as inhibition and (in)attention, and
whether these were best explained by depletion of execu-
tive resources or depletion of motivation. The stop-signal
task (SST), which generates both indices of inhibition
Electronic supplementary material The online version of this
article (doi:10.1007/s00787-017-1006-y) contains
supplementary
material, which is available to authorized users.
* Tycho J. Dekkers
[email protected]
1 Department of Psychology, University of Amsterdam,
Nieuwe Achtergracht 129B, 1018 WS Amsterdam, The
Netherlands
2 Department of Forensic Psychiatry and Complex Behavioral
Disorders, Academic Center for Child and Adolescent
Psychiatry, De Bascule, Rijksstraatweg 145, 1115
AP Duivendrecht, The Netherlands
3 Amsterdam Brain and Cognition Center, University
of Amsterdam, Amsterdam, The Netherlands
4 Department of Child and Adolescent Psychiatry, VU

University Medical Center Amsterdam, Amsterdam, The
Netherlands
5 Faculty of Law, Institute of Criminal Law and Criminology,
Leiden University, Leiden, The Netherlands
6 Department of Developmental and Educational Psychology,
Leiden University, Wassenaarseweg 52, 2333 AK Leiden,
The Netherlands
7 Department of Child and Adolescent Psychiatry, GGZ
Delfland, Center for Psychiatry, Amsterdam, The Netherlands
8 Practice for Individual, Couple, and Family Therapy
and Center for Training, De Kontekst, Van Breestraat 147HS,
1071 ZL Amsterdam, The Netherlands
9 Research priority Area Yield, University of Amsterdam,
Amsterdam, The Netherlands
http://crossmark.crossref.org/dialog/?doi=10.1007/s00787-017-
1006-y&domain=pdf
https://doi.org/10.1007/s00787-017-1006-y
1472 Eur Child Adolesc Psychiatry (2017) 26:1471–1481
1 3
Introduction
Children with attention-deficit/hyperactivity disorder
(ADHD) are characterized by inattention, hyperactivity,
and/or impulsivity, which lead to problems in multiple
domains. For example, children with ADHD have more
academic problems [1] and adverse health outcomes [2],

report lower quality of life [3], and usually have one or
more comorbid psychiatric diagnoses [4]. Several models
explaining ADHD have been proposed (see [5, 6]). One
influential model is the dual pathway model, in which
ADHD is characterized by deficits in both executive and
motivational systems [7].
With regard to the executive pathway, several meta-
analyses indicate that children with ADHD are impaired
on multiple executive functions (EF) [8–11]. For example,
response inhibition, which is regarded as one of the core,
higher order executive functions [12, 13], has repeat-
edly shown to be implicated with ADHD [11, 14]. On a
more basic pre-executive level, attention is a crucial pre-
requisite of executive functioning [13], and associations
between ADHD and attentional problems are consistently
reported (ranging from problems in sustaining attention
on lab tasks to real life attention problems [9, 14]).
With respect to the motivational pathway, many empir-
ical studies as well as theoretical models suggest aberrant
motivation in children with ADHD (see [15] for an over-
view). Some models propose that children with ADHD
have a higher reward sensitivity than controls (i.e., larger
improvement in performance related to reward; [16, 17]),
but experimental findings for this account are mixed [18].
However, a recent meta-analysis on reinforcement effects
on inhibition in ADHD indicated that (1) a large major-
ity of children, both with and without ADHD, benefited
from reinforcement and (2) this reinforcement effect was
stronger for ADHD (large effect size) than for controls
(medium effect size), suggesting differential reward sen-
sitivity between groups [16]. The authors note that only
24% of the studies found significant group × reinforce-
ment interactions in this direction, which is in line with
the mixed findings that were mentioned previously.

EF performance in children with ADHD is often more
characterized by a stronger decrease in performance over
time (time-on-task) as compared to TD controls [19]. It
has been argued that these time-on-task effects originate
in difficulties sustaining attention, which is a typical,
although not specific [20], feature of ADHD [14, 21]. In
accordance with the dual pathway model, this time-on-
task effect can be caused by degraded (EF) resources, but
it may also be possible that decreased levels of motiva-
tion explain this decrease in performance. It was shown
that time-on-task effects on working memory in ADHD
could be partly counteracted with reinforcement [22],
suggesting that they should at least partly be attributed to
decreased motivation. However, to our knowledge, it has
never been tested before whether this is also the case for
response inhibition and attention. Therefore, the current
study investigates whether time-on-task effects on inhi-
bition and attention in children with ADHD can be rem-
edied by increasing motivation.
Dual pathway models of ADHD do not directly speak
to the role of motivation on time-on-task effects. However,
the effect of motivation on time-on-task effects is central in
the literature on resource depletion in healthy adults. Some
resource depletion theorists argue that self-control capacities,
a concept highly related to EF [23], are limited, and conse-
quently, self-control performance degrades after successive
attempts (for reviews, see [24, 25]). However, others have
argued that a decline in motivational resources (i.e., “reduced
motivation to attain task goals” [26]) can also explain time-
on-task effects [27, 28], as these effects appear to be weaker
if participants are motivated [26, 29].
To sum up, the current study combines dual pathway mod-

els of ADHD and resource depletion models of time-on-task
effects in healthy adults, to assess the origin of time-on-task
effects in children with ADHD. That is, we test whether chil-
dren with ADHD are more affected by time-on-task effects
than TD children. To investigate the nature of these time-on-
task effects, depletion of resources and depletion of motiva-
tion were disentangled. Children with and without ADHD
performed twice on the stop-signal task (SST; [30, 31]),
which yields a measure of response inhibition and more
indirect measures of (in)attention. In the second task, partici-
pants were either assigned to a reinforced or a non-reinforced
condition.
First, we hypothesize degraded performance of children
with ADHD as compared to TD children in the first task and
in the second task without reinforcement (effects of group)
[8–11]. Second, we hypothesize degraded performance on
the second task without reinforcement as compared to the
first task (effect of time-on-task), and we expect this effect to
be larger in children with ADHD than in TD controls (time ×
group interaction; [19]). Third, we hypothesize a better per-
formance on the second task with reinforcement as compared
to the second task without reinforcement (effect of reinforce-
ment), and we hypothesize children with ADHD profit more
from reinforcement than TD controls (reinforcement × group
interaction; [16, 22]).
Method
Participants
ADHD participants were recruited from an academic
outpatient mental healthcare center and TD control

1473Eur Child Adolesc Psychiatry (2017) 26:1471–1481
1 3
participants were recruited from elementary schools.
In the ADHD group, children were included when they
were diagnosed with ADHD (all subtypes), according
to the assessment by expert psychologists or psychia-
trists from the academic outpatient mental healthcare
center, following DSM-IV-TR criteria [32]. There was no
exclusion based on other disorders. Children in the con-
trol group were included only when their primary care-
takers confirmed that there was no ADHD diagnosis. In
total, our sample consisted of 111 children aged between
9 and 13 years. 54 children with ADHD (45 boys, mean
age 11.2 years, SD = 1.04) and 57 children without
ADHD (27 boys, mean age 11.8 years, SD = 0.68) were
included.
When using stimulant medication, participants were
instructed not to take their medication on the day of test-
ing, to reach total washout [33]. Informed consent was
obtained from primary caretakers of all children. All pro-
cedures were in accordance with the ethical standards of
the institutional research committee and with the 1964
Helsinki declaration and its later amendments.
Materials
Response inhibition and, indirectly, attention were meas-
ured with the standard stop-signal task (SST; [30, 31]),
which is a reliable indicator of inhibition in children
with ADHD [34]. Several studies showed associations
between the SST and a wide range of real life behaviors,
e.g., associations with classroom observations of chil-
dren with ADHD [35], with teacher ratings of inattention

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