ArticlePDF Available

Embedding Experiments: Staking Causal Inference in Authentic Educational Contexts



To identify the ways teachers and educational systems can improve learning, researchers need to make causal inferences. Analyses of existing datasets play an important role in detecting causal patterns, but conducting experiments also plays an indispensable role in this research. In this article, we advocate for experiments to be embedded in real educational contexts, allowing researchers to test whether interventions such as a learning activity, new technology, or advising strategy elicit reliable improvements in authentic student behaviours and educational outcomes. Embedded experiments, wherein theoretically relevant variables are systematically manipulated in real learning contexts, carry strong benefits for making causal inferences, particularly when allied with the data-rich resources of contemporary e-learning environments. Toward this goal, we offer a field guide to embedded experimentation, reviewing experimental design choices, addressing ethical concerns, discussing the importance of involving teachers, and reviewing how interventions can be deployed in a variety of contexts, at a range of scales. Causal inference is a critical component of a field that aims to improve student learning; including experimentation alongside analyses of existing data in learning analytics is the most compelling way to test causal claims.
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
Volume 5(2), 4759.
Embedding Experiments: Staking Causal Inference in
Authentic Educational Contexts
Benjamin A. Motz 1, Paulo F. Carvalho 2, Joshua R. de Leeuw 3, Robert L. Goldstone 4
To identify the ways teachers and educational systems can improve learning, researchers need to make causal
inferences. Analyses of existing datasets play an important role in detecting causal patterns, but conducting
experiments also plays an indispensable role in this research. In this article, we advocate for experiments to be
embedded in real educational contexts, allowing researchers to test whether interventions such as a learning activity,
new technology, or advising strategy elicit reliable improvements in authentic student behaviours and educational
outcomes. Embedded experiments, wherein theoretically relevant variables are systematically manipulated in real
learning contexts, carry strong benefits for making causal inferences, particularly when allied with the data-rich
resources of contemporary e-learning environments. Toward this goal, we offer a field guide to embedded
experimentation, reviewing experimental design choices, addressing ethical concerns, discussing the importance of
involving teachers, and reviewing how interventions can be deployed in a variety of contexts, at a range of scales.
Causal inference is a critical component of a field that aims to improve student learning; including experimentation
alongside analyses of existing data in learning analytics is the most compelling way to test causal claims.
Notes for Practice
Learning Analytics, as a field, should ultimately strive to make strong causal inferences, identifying the
specific interventions that optimize and improve learning.
The most straightforward and compelling research method for supporting causal inference is
In this article, we advocate for embedding experiments within pre-existing learning contexts, in order
to improve the strength of causal claims in learning analytics, and also to close the research/practice
We review practical matters in the design and deployment of embedded experiments and highlight the
benefits of including experimentation in the learning analytics toolkit.
Causal inference, experiments, research design, ethics , A/B testing.
Submitted: 16.10.18 Accepted: 06.02.18 Published: 05.08.18
Corresponding author 1Email: Address: Department of Psychological and Brain Sciences, Cognitive Science Program
Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, United States ORCID ID: 0000-0002-0379-2184,
2Email: Address: Human-Computer Interaction Institute, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh,
PA 15213, United States ORCID ID: 0000-0002-0449-3733
3Email: Address: Human-Computer Interaction Institute, Vassar College, 124 Raymond Avenue, Poughkeepsie, NY
12604, United States
4Email: Address: Department of Psychological and Brain Sciences, Cognitive Science Program, Indiana University, 1101
East 10th Street, Bloomington, IN 47405
1. Causality in Learning Analytics
Learning analytics, as a field, is universally defined with a specific purpose in mind: optimizing and improving student
learning. Towards this goal, research in learning analytics should not only explain learning processes within our educational
systems, but should also bridge the research and practice gap to produce “actionable intelligence” (Norris, Baer, Pugliese, &
Lefrere, 2008; Arnold, 2010; Elias, 2011; Clow, 2012, 2013) developing systems, predictions, interventions, or insights to
improve outcomes in authentic learning environments. As members of a learning analytics research community, we should
aim to make strong, actionable, causal inferences: my research suggests that if you do this, student outcomes will improve.
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
Although a key goal of learning analytics is to ultimately make causal inferences, the conventional methods of learning
analytics have excluded standard research tools for supporting such inferences. Until recently, most characterizations of
learning analytics research methods were limited to observation of student data generated from real educational systems (Cope
& Kalantzis, 2015b), with inferences gleaned primarily from statistical modelling, visualizations, and dashboards based on
these extant data resources (Baker & Yacef, 2009; Bienkowski, Feng, & Means, 2012; Chatti, Dyckhoff, Schroeder, & Thüs,
2012; Siemens, 2012, 2013; Dietz-Uhler & Hurn, 2013; Khalil & Ebner, 2015; for a constructive critique of these
characterizations, see Lodge & Corrin, 2017). The booming availability of large datasets, offering the ability to quickly search
and summarize records across an entire student population’s educational landscape, created enticing new research
opportunities, typically emphasizing the discovery of relationships using exploratory data analysis (Enyon, 2013; Baker &
Inventado, 2014) and predictive models of future outcomes (Macfadyen & Dawson, 2010). These analyses can reveal important
and useful relationships that have previously been completely unobservable. But why stop there?
We suggest there is something missing, an epistemological gap, in the conventional view of the learning analytics toolkit.
Analyses of existing datasets can play an important role in detecting and discovering causal patterns, but an indispensable
aspect of this research, if we truly aim to create reliable actionable intelligence, is the conduct of experiments. In addition to
harnessing data traces, learning analytics should rigorously explore ways of manipulating these traces, conducting experiments
to evaluate an action’s effect on intended outcomes.
We are not the first to voice this argument. Developing Kolb’s (1984) theoretical work, Clow (2012) prominently asserted
that, once learning analytics produces actionable intelligence, a critical next step is to develop this insight into an intervention,
actively experimenting to examine whether an action causes a change in learner behaviour (see also Koedinger, Stamper,
McLaughlin, & Nixon, 2013). Similarly, Reich (2015) argued that, without experimental intervention research, the causal links
between aspects of course design and student performance are unclear. Some have also recently noted that online courses, in
particular, provide researchers with the opportunity to easily implement experiments that clarify the relationship between
design choices and student achievement (Williams & Williams, 2013), as well as addressing broader questions about
educational practices (Kizilcec & Brooks, 2017).
The benefit of experimentation is that it represents the single most persuasive way to support a causal inference (Shadish,
Campbell, & Cook, 2002). This is because, in an experiment, exposure to a causal antecedent (a learning activity, a new
technology, an advising strategy, etc.) is manipulated by the researcher, enabling direct assessment of whether some
consequence (e.g., a learning outcome) can be causally attributed to the specific change in treatment. The hallmark of an
experiment is that the unit under observation (a student, a teacher, a class, etc.) should be randomly assigned to different
conditions. In this way, there should be no differences between treatment groups other than the experimental treatment itself.
Nevertheless, experimenters should be sensitive to the possibility that some consequential difference other than the
treatment could be lurking between randomly assigned comparison groups. Statistical analyses are used, in part, to quantify
the likelihood of this error, and the possibility of imbalance can be minimized by using large samples and only accepting results
that meet conservative statistical thresholds. Additional methods for randomly assigning treatments to subgroups within the
sample (e.g., blocking; Higgins, Sävje, & Sekhon, 2016), or repeating random assignment until balance is achieved on pre-
specified dimensions (rerandomization; Morgan & Rubin, 2012) may further mitigate the possibility of imbalance. These may
be uniquely appropriate techniques in learning analytics, where researchers typically have more background data on research
subjects than in other fields. Alternatively, a more common approach would be to include model-based estimators (e.g.,
regression adjustments) to control for other variables that might produce imbalance in the comparison groups. None of these
techniques fully eliminates the possibility of error in random assignment, but with appropriate design and analysis choices,
experimenters can minimize this risk.
In total, evidence from an experiment satisfies the strong requirements of causal inference by demonstrating that changes
in treatment modify an outcome in a specific direction (ruling out reverse causality) while minimizing (by randomization and
other methods) the possibility that some other factor caused changes in the outcome.
It bears mention that these conditions might also be satisfied (to some degree) using quasi-experimental or even non-
experimental methods. Particularly when taking into account the temporal ordering of variables and causally relevant
background variables, some observational analyses are able to provide distinguishing evidence for causal relationships over
mere covariance (Pearl & Verma, 1995; Spirtes, Glymour, & Scheines, 2000; Russo, 2010; Murnane & Willett, 2010; Kumar,
Clemens, & Harris, 2015). As Tufte (2003) pronounced, “Correlation is not causation, but it sure is a hint” (p. 4). Our goal is
not to suggest that experimentation is the only way to offer empirical support for a causal claim or to suggest that it is infallible
(Imai, King, & Stuart, 2008), but to assert that it is a uniquely powerful tool when assessing the effect of an intervention
particularly so, considering the goals of learning analytics and educational research in general (US Department of Education,
2016, 2017).
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
This assertion is not without historical controversy in the broader study of teaching and learning (Angrist, 2004). For
example, theorists have questioned whether causality is a meaningful theoretical construct in education (e.g., Maxwell, 2004),
whether control is possible in an educational setting (e.g., Barab & Squire, 2004), and whether it is feasible to identify
individual causal relationships for complex problems in education (e.g., Morrison & van der Werf, 2016). These are reasonable
concerns (which similarly apply to descriptive and correlational work), and programs of experimental research in learning
analytics should certainly aim to make precise and meaningful theoretical claims (Wise & Shaffer, 2015), should utilize
research implementations that have external validity (Lockyer, Heathcote, & Dawson, 2013), and should be sensitive to the
complexity of educational systems (Koedinger, Booth, & Klahr, 2013). Experimentation that includes these features can be
difficult to implement and is not always possible. However, the challenges of conducting experiments in education do not
justify ignoring the epistemological value of experiments in education.
Even beyond providing strong evidence for a causal relationship, experiments can also help by pinpointing the precise
conditions under which an outcome should be observed. As such, the details of an experiment can help researchers evaluate
whether a causal relationship should generalize to new situations. It is likely that additional variables (such as learner
demographics, the educational context, or the nuances of the situation) will moderate the effect of a treatment. The “mileage”
of any intervention may vary between different situations, and controlled experiments can help prevent overgeneralization by
providing clear estimates of a causal effect within a specific context (e.g., Kizilcec & Cohen, 2017). For these reasons, we see
tremendous promise for the field of learning analytics researchers deploying experiments in a diversity of learning contexts.
Why, then, has experimentation only recently started to appear in catalogues of the methods of learning analytics? To our
knowledge, no learning analytics researcher has ever voiced an argument against experimentation, but we can postulate a few
concerns. Perhaps experiments, traditionally associated with laboratories, rigour, and control, seem incompatible with the
opportunities afforded by the surge in big, messy, authentic student data. Perhaps the act of manipulating exposure to different
educational interventions seems unethical in real classes. Perhaps an experimental operationalization of a learning treatment
would be considered artificial or unrepresentative of natural instruction. And perhaps a randomly assigned learning intervention
seems too challenging to implement at scale.
These hurdles are not insurmountable, and the benefits of explicitly including experimentation in the “learning analytics
cycle(Clow, 2012) greatly outweigh the challenges. In this article, we address each of these postulated challenges, and
ultimately provide a framework to expand the scope of learning analytics research methodology, from pure extant data mining
to the inclusion of embedded experimental research that aims to manipulate student outcomes and draw stronger causal
2. Embedded Experiments
Thus far, we have argued for the unique inferential power that experimental interventions have for determining causality, and
that they should be a major component in the learning analytic toolkit. Assuming the acceptance of this general claim, a logical
next question becomes: What would these experiments look like?
Consistent with the focus of learning analytics on measuring learner data within educational contexts such as classrooms,
museums, online tutoring, and on-the-job training, we would like to advocate for embedded experimentation. By embedded
experimentation we mean experiments conducted within pre-existing educational contexts, including both formal classrooms
and informal learning settings, including both schools and workplace environments, and making use of authentic learning
materials and assessment instruments that are relevant to the pre-existing learning goals.
The notion of embedded experimentation shares considerable common ground with proposals for in vivo experiments
(Koedinger, Corbett, & Perfetti, 2012; Koedinger et al., 2013a), but we favour the “embedded experimentation” term because
it emphasizes that learning is a major activity across the lifespan, in both educational and workplace training contexts, and in
both informal and formal settings. Diverse and elaborate institutions have been established to foster learning, including
classrooms, museums, studios, workshops, special interest groups, and online tutorials, and these offer unique opportunities to
study societally relevant learning. By bringing experimentation to these contexts by embedding experiments in these pre-
existing institutions we can assure that the results are pertinent to at least some naturally occurring situations, and we can
take advantage of the learning infrastructures that have been created with much expense and time. While learning in both
research laboratories and university classrooms is arguably in vivo in that it is taking place in an intact, whole organism, only
learning in university classrooms would count as embedded learning. Embedded learning focuses on studying learning in the
“wild” — in the natural, albeit socially constructed contexts in which it has developed on its own, independent of researchers’
theories and paradigms.
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
To many ears, the very phrase “embedded experimentation” may sound like an oxymoron. Experiments may be assumed
to be what researchers do within laboratory contexts: Learners are brought into a laboratory, settled into their own private
cubicle, presented with artificial materials to be learned, and subsequently tested on their acquisition and generalization of
these materials. Although this is the dominant paradigm within cognitive psychology, there is also a long, if sometimes
forgotten history of conducting learning experiments in pre-existing contexts outside of the psychology laboratory (Bryan &
Harter, 1899; Hall, 1891).
Embedding experiments within already established learning contexts has several advantages over laboratory investigations.
First, learners are less likely to be self-conscious and more likely to use the kinds of learning strategies that they normally
employ. Laboratories are unfamiliar environments that almost inevitably put the learner at a disadvantage in terms of authority,
control, and comfort. Second, if a researcher wants to better understand likely learning outcomes in a specific context, it is
wise to study them within that context. There have been many well-documented cases in which learning processes and
outcomes differ profoundly across cultures, schools, and contexts (see Medin & Bang, 2014). Third, the archetype of the
solitary learner acquiring information in a generic context is never, in fact, realized (Greeno et al., 1998). Learning is always
situated in a context, and whatever learning takes place is always an interaction between the learner and their context.
Embedding experiments within those contexts allows a researcher to understand how an intervention affects the broader,
distributed system of learning. For example, an intervention that encourages students in a class to talk to their peers about the
course material may improve not only their own understanding, but the understanding of their peers as well (Crouch & Mazur,
2001). These indirect benefits would only be discoverable when the peer-instruction intervention is deployed in the context of
a course complete with other students, and not when the students are isolated in their own laboratory cubicles.
The core characteristic of an embedded experiment is that some learners learn with one form of the intervention while other
learners learn with another form of the intervention. This comparison between interventions may or may not resemble
traditional laboratory experiments in which compared conditions are selected to differ in only one way. By virtue of this
flexibility in choosing apt comparisons, we are more optimistic about the feasibility of conducting genuine experiments in
embedded contexts than others who have emphasized the expense and difficulty in deploying randomized control trials, or
RCTs (see Sullivan, 2011). Our optimism stems from an open, ecumenical stance towards experimental design. Different
experimental designs are appropriate for different contexts, and if one permits oneself flexibility in terms of design choices,
then one can usually find an embedded experimental design that warrants qualitatively stronger causal inference than is possible
without intervening on the educational system (Pearl, 2000). Our optimism also stems from the surging availability of online
data traces in contemporary educational systems; an experiment that randomly assigns different versions of an online
homework activity can yield detailed behavioural data on-par with what had previously only been possible in a laboratory with
specialized software (Cope & Kalantzis, 2015a).
One important design consideration concerns the choice of the treatment conditions to compare. For the purposes of
isolating a key contributing factor in a learning context, establishing very similar groups that differ only on that factor is
desirable. By keeping the materials and the student population constant across conditions, differences between even subtly
different experimental conditions can be detected that would otherwise be missed. For example, Roediger, Agarwal, McDaniel,
and McDermott (2011) conducted a series of embedded experiments to compare the benefit of frequent quizzing with the
benefit of re-reading. For the study, the authors selected a subset of different materials covered in the students’ curricula and
normal class activities to be included in the study. Pre-test and post-test measures were specifically created for these materials
and different materials were assigned to be quizzed or re-read for different individual participants (i.e., which subset of
materials were re-read or quizzed varied across students). This strategy of designing minimally contrastive conditions is
particularly useful when: 1) a researcher can identify and manipulate a key factor governing learning that is likely to arise in
many different learning contexts, 2) the choices of factor levels (i.e., quizzed vs re-read for the factor study type”) along
different factors (i.e., curriculum topic) are at least partially independent of each other, and 3) the difference in learning
outcome likely to be found for different factor levels is small-to-moderate and may be swamped by variation along many other
While minimally contrastive interventions are valuable for isolating the effect of a single contributing factor, they are by
no means the only game in town, and other experimental designs are better in other contexts. One alternative, oftentimes
effectively employed after several influential minimally contrastive interventions have been identified, is to compare a
condition in which all empirically favourable levels of factors are combined on a “Dream Team” package of pedagogical
changes and compared to a condition in which neutral or status quo levels of these factors are combined. A good example of
this strategy was adopted by the National Research & Development Center on Cognition & Mathematics Instruction
in their
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
effort to create an improved mathematics textbook by applying established principles of the cognitive science of learning
(Booth et al., 2017). Although this approach contrary to the minimal contrastive approach does not allow a single factor
to be unambiguously identified as impacting learning outcomes, it offers the countervailing advantage of determining whether
a set of independent design decisions complement each other when combined so that the entire system confers pedagogical
benefits. Furthermore, the “Dream Team” condition may often show large, statistically robust benefits even when each of the
factors has only a small effect size. If the package of changes does show a robust benefit, then subsequent experiments
employing minimally contrastive interventions can be deployed to isolate the most potent ingredients of the composite
Another possible way to choose the interventions to compare is inspired by the notion of “pragmatic trials” in medicine.
Contrasted with “explanatory trials” designed to test if and how an intervention confers medical benefits compared to placebo
controls using RCT, pragmatic trials investigate whether an intervention confers benefits in real life contexts compared to other
viable alternatives (Patsopoulos, 2011). For example, in testing whether liposuction is an efficacious treatment for obesity,
comparing its effects to those produced by putting patients on a regular schedule of exercise would count as a pragmatic trial.
These strategies for treating obesity differ in a variety of important ways, and for that reason, even if one strategy, say exercise,
is clearly superior to the other, one still would not know whether this is because it requires the active involvement of the patient,
does not require invasive surgery, is persistent, or some other factor. Still, if one is a doctor trying to devise a sensible long-
term policy for treating patients, the results from this pragmatic trial may be exactly what one is looking for. Likewise, teachers
trying to choose between different curricula, tutoring systems, or textbooks may simply need an experimental “cook off”
comparison of some of the most prime facie plausible possibilities, testing whether one reasonable instructional design is better
than another. An example of this type of approach to embedded experimentation is the study conducted by Kirchoff, Delaney,
Horton, and Dellinger-Johnston (2014) to test the efficacy of a computer-based perceptual training intervention. The authors
tested whether training software aimed at improving student recognition of plants (that incorporated several design features
known to benefit perceptual training) would improve student learning in a plant systematics course. To this end, they compared
learning outcomes when students used the software and when they used status quo classroom practices. Although this study
does not allow one to determine which feature(s) of the software contribute to improved performance, the results do suggest
that perceptual training can contribute to improved conceptual learning.
One advantage of embedded over laboratory experiments is that they encourage researchers to consider comparing
interventions that make sense in real world contexts. For example, cognitive psychologists studying concept learning in the
laboratory often make the assumption that learners must learn a set of concepts via pure induction by seeing examples,
attempting to categorize the examples, and then receiving feedback on the correctness of their categorization (Goldstone,
Kersten, & Carvalho, 2017). Perhaps this assumption is a vestige from early animal learning research (in which it would be
impossible to provide verbal instruction to a rat, for example, that shape but not brightness is relevant), but in educational
contexts this represents a rather ineffective pedagogical strategy. By contrast, teachers, coaches, and parents have all found
that even though wisdom cannot always be directly told to learners (Bransford, Franks, Vye, & Sherwood, 1989), well-crafted
words, rules, and instructions can often be used to dramatically expedite both performance and understanding (Klahr & Nigam,
2004; Ellis, 2005). Laboratory-focused researchers might end up comparing artificial learning conditions, such as perfect
alternation between concepts to be learned (e.g., sequencing the examples of two concepts in the order ABABABAB) versus
perfectly blocked concepts (e.g., AAAABBBB), without adequate acknowledgment of the possible irrelevance of this
comparison for real world learning environments. Researchers engaging in embedded experimentation are more likely to
consider interventions that generally conform to educational best practices such as well-timed instructions, informative
feedback, verbal help, and hints.
The general point is that choosing minimally contrastive interventions to compare is indeed an appropriate experimental
design strategy, but it is not the only important consideration. It is also appropriate to consider the real-world relevance of the
interventions to actual instructional practice, and the current state-of-the-art in teaching of the discipline. For example, if it is
generally appreciated in a teaching community that simple re-reading is not an effective study method, then an experiment that
compares re-reading as part of a group versus independent re-reading will risk being largely irrelevant to practice. The choice
of interventions to compare should be based on their prevalence, demonstrated efficacy, and practicality, in addition to their
precision in isolating single factors, depending on the research question.
In sum, embedded experiments can support a variety of causal inferences. In some cases, the inference will be specific to
a particular factor that affects learning outcomes. In other cases, the inference will be about a general approach or strategy
without isolating the factor(s) responsible for the improvement. The nature of a particular embedded experiment will depend
on the theoretical goals of the research and the practical constraints of the educational situation.
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
3. Ethical Considerations
The notion of intentionally manipulating a learner’s educational experience for the purpose of research raises an important
ethical question: What if condition B is reliably inferior to condition A? Has the research harmed the learners in this case?
Before addressing this question specifically, consider for a moment that teachers, at all levels, are encouraged, if not expected,
to experiment in their classrooms routinely. Experimenting with different instructional methods is viewed as a positive feature
of teachers’ professional development and growth (Guskey & Huberman, 1995), where a teacher tries new things (on a full
student cohort) and reflects on the efficacy of the new approach. Under this scheme, whether new tactics “work” can only be
judged by subjective reflection, because there is no balanced comparison condition to make valid analytical contrasts. Thus,
unbeknownst to them, students in practically all classrooms are participants in a vast enterprise of uncontrolled
experimentation. This enterprise carries the same risk of inferior treatment as what we are proposing (and perhaps more,
because negative effects might not be readily apparent to subjective reflection) but affords none of the benefits of causal
inference. Perhaps ethical considerations do not hinge on whether experiments should be embedded in classrooms, but whether
well-designed, controlled experiments should be embedded.
At the most basic level, a manipulation that is known to negatively impact learning would be of no use as a comparison
condition in an embedded study. Similarly, unnaturally deprived control conditions would be inappropriate for experimental
contrast, as these would overestimate the manipulation’s performance against realistic alternatives (as discussed in the previous
section). At the very least, an embedded experiment should contrast sensible design options, and should not administer a
treatment known to or believed to potentially cause decrements in learning outcomes.
Even so, a practical way to avoid any possibility that a group will experience disproportionate risk is to administer all
treatments to all groups but staggered in time. A crossover or delayed treatment design allows one to compare a group that
received a treatment with a group that has not yet received that treatment. For example, in examining the benefits of instruction
using library archives, Krause (2010) embedded an experiment in an undergraduate history class: one half of the class initially
received the experimental exposure to archival instruction, and the other half received the same instruction and assignments
four weeks later. Incidentally, in addition to addressing potentially ethical issues, this approach might also improve the
statistical and exploratory power of the study, potentially allowing replications within the same cohort (Heath, Kendzierski, &
Borgida, 1982).
Rather than avoiding risks (by balancing the different treatments within comparison groups), another option would be to
simply minimize possible risks of different treatment. An embedded experiment could focus on a relatively small aspect of the
course, so that any differences between groups are practically negligible for an individual student. For example, an intervention
could be designed to only affect performance on just a few target questions on a single test, such as in the study we mentioned
above testing the benefits of frequent quizzing (Roediger et al., 2011). One of the benefits of scale (see next section) is the
opportunity to embed experiments with a very large number of students, making it possible to measure reliable differences in
treatment, even with small effect sizes. With unknown consequences of treatment, it is best to keep modest aims when
embedding an experiment in a real learning context. For example, in an embedded experiment including over 2,000 students,
Carvalho, Braithwaite, de Leeuw, Motz, & Goldstone (2016) tested whether the way students choose to organize their study
influenced their learning outcomes. The authors did this by choosing a single class topic (measures of central tendency) for
their intervention and included only four test questions (on a single exam pertaining to that topic) as a post-test measure. The
large sample allowed inferences to be drawn from a short intervention with a small effect size.
How do the risks of embedded experimentation compare with laboratory experimentation? Arguably, generalizing from
small-scale laboratory studies with limited samples carries a bigger potential of deploying detrimental interventions. Instead,
by embedding experiments in authentic contexts, interventions are tested in natural settings using appropriate sample sizes that
represent the diverse population of students. This means that embedded studies have the potential benefit of a more inclusive
study setting capable of reaching populations not typically studied in the laboratory.
In our view, with proper care as described above, this type of study risks no greater harm than any number of pedagogical
decisions that teachers make every day. By working with teachers to create truly embedded studies, using appropriate tools
and large-scale data collections, we believe the benefits to the research participants can be maximized. Under these terms, one
might argue that conducting an experiment is more ethical than uncontrolled pilots in educational environments, particularly
when one is uncertain about which of several plausible interventions to implement. If an intervention is worth doing, it’s worth
systematically testing its effects against reasonable alternatives. By conducting an actual experiment on an intervention’s
efficacy, its benefits will be more convincing to other researchers and teachers. We suggest there is risk in not conducting
experiments: genuinely beneficial instructional innovations will be ignored if they are not supported by compelling, rigorous
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
4. Embedded Experiments at Scale
It is possible to conduct embedded experiments at all scales. Whereas small scale “drop-in” studies that involve one small
manipulation in a single classroom are common (Arnold et al., 2017; Butler, Marsh, Slavinsky, & Baraniuk, 2014), it is also
possible to create carefully controlled studies embedded in educational contexts that span several classes, schools, populations,
and geographical areas. For example, it is possible to perform the same experimental manipulation in different content areas
(Cantor & Marsh, 2017), across different classes of the same course (Carvalho et al., 2016), in large-scale massive online
courses (Chen, Demirci, Choi, & Pritchard, 2017; Zheng, Vogelsang, & Pinkwart, 2015; Kizilcec, Pérez-Sanagustín, &
Maldonado, 2016; Williams & Williams, 2013), or across multiple schools (Fyfe, 2016; Koedinger & McLaughlin, 2016).
One of the powers of embedded experimentation lies in combining it with institution-level data collection in the learning
analytics tradition, commonly by using online learning platforms or massive courses (e.g., Renz, Hoffmann, Staubitz, &
Meinel, 2016; Heffernan & Heffernan, 2014). Larger studies integrating across multiple populations will support more
sensitive comparisons and/or more robust causal inferences. Moreover, when outcome measures are joined with existing
institutional data, these can also provide better information about demographic factors that correlate with observed effects.
Still, although large-scale embedded experiments have great potential, scaling up to a large coordinated experiment across
multiple populations can present substantial challenges.
The internet is an obvious tool for solving the scaling challenge. The most straightforward use of the internet is as a
distribution platform. For example, the PhET Interactive Simulations project (Wieman, Adams, & Perkins, 2008) has
developed dozens of simulations for teaching concepts in STEM fields. These simulations can be accessed by any teacher or
researcher through a web browser, and used as part of a classroom activity or an embedded experiment (Finkelstein et al.,
2005; Moore, Herzog, & Perkins, 2013). Going a step further, the internet can also be used to create efficient coordination for
collecting and aggregating data across multiple classrooms. One example of this approach is the ASSISTments platform
(Heffernan & Heffernan, 2014). Researchers can use ASSISTments to develop student activities that contain a manipulation
of one or more factors and collect data from students in classrooms. Teachers are (partially) involved in the process because
they can choose which activities in ASSISTments are relevant for their class.
A more flexible approach is to create custom experiment materials using web-friendly technology so that the experiment
can be deployed online and yet retain the flexibility of traditional classroom activities. Increasingly, cognitive scientists are
utilizing online tools to conduct experiments over the internet (Stewart, Chandler, & Paolacci, 2017), and several platforms
have been created to make the development of custom online experiments easier (de Leeuw, 2015; Henninger, Mertens,
Shevchenko, & Hillbig, 2017). Embedded experiments using online survey platforms (Day, Motz, & Goldstone, 2015), and
custom JavaScript (Carvalho et al., 2016) highlight the utility of this approach. However, building an online experiment still
requires a relatively burdensome amount of technical knowledge, and so is presently only available to researchers and teachers
who themselves have expertise, or a substantial budget.
Even when studies themselves are performed at small scale in isolation, open-science tools like DataShop (Koedinger et
al., 2010) and LearnSphere
where data from embedded studies can be stored, shared, combined, and analyzed can
facilitate the kinds of statistical power that would be possible with large-scale experiments (e.g., Koedinger & McLaughlin,
2016; Koedinger, Booth, & Klahr, 2013). These tools exemplify an alternative approach to scaling up embedded experiments:
individual researchers and teachers conduct experiments at relatively small scales, but data collection is aggregated across
research sites to realize the power of large-scale experimentation. In psychology, a series of ManyLabs projects have allowed
researchers to pool resources in this way to investigate questions best answered with distributed large-scale experiments (Klein
et al., 2014; Ebersole et al., 2016; Frank et al., 2017).
In an ideal world, the technology for creating large-scale embeddable experiments would be user-friendly enough that
teachers and researchers can use it as they would use any other piece of software to create classroom activities. For example,
we imagine a world where a teacher could decide that she wants to compare different strategies for practicing factoring
polynomials and is able to create a homework assignment that randomly assigns different strategies to students. Perhaps the
teacher deploys this experiment in one or two classes of 30 students, finds the exercise to be generally useful, but the sample
size too small to draw any robust conclusions. The teacher shares the materials with her colleagues, who do no additional work
other than assigning the work to their students, perhaps by directing them to a website or a module in a learning management
system. The data from multiple classes is then aggregated and made available for analysis using hierarchical models that take
into account different levels of variability in this type of nested data (for example, independent variation in individual student
knowledge, as well as classroom differences, teacher differences, etc.).
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
This scenario is certainly possible with todays technology (Severance, Hanss, & Hardin, 2010), but it is neither easy nor
commonplace to run embedded experiments. For this vision to become a reality, we need technology that enables highly
customizable experiments and instructional materials, that is accessible to teachers and researchers without substantial
additional training or expertise, and that can operate at any scale. Currently available options often have one or two of these
features, but not all three. This, of course, does not mean that embedded experiments are not feasible with current technology,
but rather that there is no universal solution yet.
5. Involvement of Teachers
An experiment involves comparing different treatment conditions, and considering that these treatments are, fundamentally,
educational tools and interventions, teachers ought to be involved in the design and analysis of these embedded experiments.
This may seem a blatantly obvious and unnecessary statement, seeing as how many learning analytics researchers are teachers
ourselves. But in a field that has traditionally defined itself by analysis of second-hand observational data, a shift toward
experimental manipulation within live learning settings requires additional involvement of teachers as content experts and as
users in the areas being investigated. Specifically, we advocate for increased involvement of teachers in embedded experiments
so that interventions are authentic and feasible.
The results of an embedded experiment must be feasible in order to advance the goal of optimizing and improving learning.
Just because an experiment is embedded in an authentic pre-existing learning environment does not mean that the experimental
manipulation is useful. The results of a well-controlled experiment that pays money to real students for time spent studying,
for example (see Fryer, 2011), would be inapplicable to the vast majority of learning environments, because they wouldn’t be
able to afford to monetize self-regulated studying behaviours at scale. Effectively embedding an experiment in a learning
environment means more than just administering treatment to real students; it also means developing a treatment that fits within
the constraints of the learning environment. Teachers should be involved to help identify these constraints, ensuring that the
experimental manipulation could be realistically implemented in similar environments, which is an important aspect of
providing actionable intelligence. Again, an analogy can be drawn to medicine, where many practicing physicians are also
involved in research, and the participation of doctors who are also seeing patients is a good thing, benefiting the clinicians, as
well as the quality of the research (Lader et al., 2004; Rahman et al., 2011). It helps identify implementation challenges, and
bottom-up ideas for treatments. The same is analogously true for teachers.
Teachers, as content experts, can also crystalize and constrain our assumptions about how experimental interventions are
appropriately embedded into our courses’ and institution’s educational goals (Gašević, Dawson, & Siemens, 2015; Bakharia
et al., 2016). In this way, the involvement of experienced teachers will help learning analytics researchers avoid
overgeneralization and build precision, tailoring experiments to address precise and practical questions about learning. This is
important because different learning goals require different teaching moves; an experiment demonstrating a reliable effect in
one domain may not generalize to other domains, and this is true at many levels of granularity (Gašević, Dawson, Rogers, &
Gašević, 2016). For example, at a very coarse level, how we teach skills is not the same as how we teach declarative knowledge.
At a very fine level, there may be uniquely useful models for teaching specific topics, like teaching fractions with pizza slices,
or teaching the genetics of inheritance using Punnett Squares. Experimental interventions should be sensitive to these
contingencies, avoiding manipulations that are orthogonal to the learning goals, while leveraging best-practice teaching
approaches within the discipline so that the treatment is authentic and broadly feasible.
For STEM educators, the contingencies of what “works” when teaching different forms of knowledge (e.g., computer
science, biology, engineering, physics, etc.) have catalyzed the emergence of a new field discipline-based educational
research (DBER; National Research Council, 2012). Among the tenets of DBER is the view that disciplinary expertise is a
core component of learning research, and that consideration of how students learn in different disciplines does not lead to the
degradation of this research. After all, there is not a one-size-fits-all approach to education. Similarly, embedded experiments
may aptly uncover different causal patterns in different learning contexts. As such, increasing teacher involvement in learning
analytics experimentation can help yield more precise theories, hypotheses, and inferences.
Another product of learning analytics (besides actionable knowledge of learning processes) may be institution-wide data-
driven dashboards and visualizations to inform teachers, advisors, and students themselves about learning behaviours and
student properties (Govaerts, Verbert, Duval, & Pardo, 2012; Motz, Teague, & Shepard, 2015; Duval, 2011; Tervakari, Silius,
Koro, Paukkeri, & Pirttila, 2014). In this mode, learning analytics is responsible for providing an analytical viewport to improve
teaching and learning, enabling a user to become better-aware of student propensities (Verbert, Duval, Klerkx, Govaerts, &
Santos, 2013). Such a lens might be useless if teachers were not involved in its development. For example, as Lockyer and
colleagues (2013) observed, the value of learning management system (LMS) data to predict student success is limited to
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
academic disciplines that make heavier use of digital infrastructure for coursework. When the goal of learning analytics is to
produce such a lens, teachers should be involved, both as designers of the system and as users in an embedded experiment
pilot, so that the visualization tool is congruent with classroom practice, and so that the tool augments teaching and learning
effectively (Plaisant, 2004).
6. Conclusion
The understanding that comes from embedded experiments at scale is an indispensable element of a research enterprise that
aims to improve learning. It allows us not only to understand the causal relationship between an intervention and the learning
outcomes, but also to uncover its limitations when it might work differently in different implementations. By embedding
experiments in real educational contexts, one can also uncover treatment effects that were not suggested by previous theory or
by laboratory experimentation, and test predictions suggested by exploration of existing data. In the end, embedded large-scale
experimentation should play a fundamental role in the learning analytics toolkit, bridging research and practice, and helping
to identify better learning interventions, better models of learning, and better suggestions for teaching and advising practice.
The authors are grateful to two anonymous reviewers whose expert commentary elevated the quality of this article.
Declaration of Conflicting Interest
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Support for this work comes from the “Learning: Machines, Brains, and Children” Emerging Area of Research Initiative at
Indiana University.
Angrist, J. (2004). American education research changes tack. Oxford Review of Economic Policy, 20(2), 198212.
Arnold, K. E. (2010). Signals: Applying academic analytics. EDUCAUSE Quarterly, 33(1).
Arnold, K., Umanath, S., Thio, K., Reilly, W., McDaniel, M., & Marsh, E. (2017). Understanding the cognitive processes
involved in writing to learn. Journal of Experimental Psychology: Applied, 23(2), 115127.
Baker, R., & Inventado, P. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.),
Learning Analytics: From Research to Practice (pp. 6175). New York: Springer.
Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of
Educational Data Mining, 1(1), 317.
Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gašević, D., Mulder, R., Williams, D., Dawson, S., & Lockyer, L.
(2016). A conceptual framework linking learning design with learning analytics. Proceedings of the 6th International
Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 329338). New
York: ACM.
Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. Journal of the Learning Sciences,
13(1), 114.
Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and
learning analytics: An issue brief. Washington, DC: US Department of Education, Office of Educational Technology.
Booth, J., McGinn, K., Barbieri, C., Begolli, K., Chang, B., Miller-Cotto, D., Young, L., & Davenport, J. (2017). Evidence
for cognitive science principles that impact learning in mathematics. In D. Geary, D. Bearch, R. Ochsendorf, & K.
Koepke (Eds.), Acquisition of Complex Arithmetic Skills and Higher-Order Mathematics Concepts (Vol. 3, pp. 297
327). Cambridge, MA: Academic Press.
Bransford, J. D., Franks, J. J., Vye, N. J., & Sherwood, R. D. (1989). New approaches to instruction: Because wisdom can’t
be told. In S. Vosniadou & A. Ortony (Eds.), Similarity and Analogical Reasoning (pp. 470497). New York:
Cambridge University Press.
Bryan, W., & Harter, N. (1899). Studies on the telegraphic language: The acquisition of a hierarchy of habits. Psychological
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
Review, 6(4), 345375.
Butler, A., Marsh, E., Slavinsky, J., & Baraniuk, R. (2014). Integrating cognitive science and technology improves learning
in a STEM classroom. Educational Psychology Review, 26.
Cantor, A., & Marsh, E. (2017). Expertise effects in the Moses illusion: Detecting contradictions with stored knowledge.
Memory, 25(2), 220230.
Carvalho, P., Braithwaite, D., de Leeuw, J., Motz, B., & Goldstone, R. (2016). An in vivo study of self-regulated study
sequencing in introductory psychology courses. PLOS ONE, 11(3), e0152115.
Chatti, M., Dyckhoff, A., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal
of Technology Enhanced Learning, 4(5/6), 318331.
Chen, Z., Demirci, N., Choi, Y.-J., & Pritchard, D. (2017). To draw or not to draw? Examining the necessity of problem
diagrams using massive open online course experiments. Physical Review Physics Education Research, 13, 010110.
Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. Proceedings of the 2nd International Conference
on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 134138). New
York: ACM.
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683695.
Cope, B., & Kalantzis, M. (2015a). Sources of evidence-of-learning: Learning and assessment in the era of big data. Open
Review of Educational Research, 2(1), 194217.
Cope, B., & Kalantzis, M. (2015b). Interpreting evidence-of-learning: Educational research in the era of big data. Open
Review of Educational Research, 2(1), 218239.
Crouch, C., & Mazur, E. (2001). Peer instruction: Ten years of experience and results. American Journal of Physics, 69,
Day, S., Motz, B., & Goldstone, R. (2015). The cognitive costs of context: The effects of concreteness and immersiveness in
instructional examples. Frontiers in Psychology, 6, 1876.
de Leeuw, J. R. (2015). jsPsych: a JavaScript library for creating behavioral experiments in a Web browser. Behavior
Research Methods, 47(1), 112.
Dietz-Uhler, B., & Hurn, J. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective.
Journal of Interactive Online Learning, 12(1), 1726.
Duval, E. (2011). Attention please!: Learning analytics for visualization and recommendation. In P. Long, G. Siemens, G.
Conole, & D. Gašević (Eds.), Proceedings of the 1st International Conference on Learning Analytics and Knowledge
(LAK ʼ11), 27 February–1 March 2011, Banff, AB, Canada (pp. 917). New York: ACM.
Ebersole, C., Atherton, O., Belanger, A., Skulborstad, H., Allen, J., Banks, J., Baranski, E., … & Nosek, B. (2016). Many
Labs 3: Evaluating participant pool quality across the academic semester via replication. Journal of Experimental
Social Psychology, 67, 6882.
Elias, T. (2011). Learning analytics: Definitions, processes and potential.
Ellis, R. (2005). Principles of instructed language learning. System, 33, 209224.
Enyon, R. (2013). The rise of big data: What does it mean for education, technology, and media research? Learning, Media
and Technology, 38(3), 237240.
Finkelstein, N. D., Adams, W. K., Keller, C. J., Kohl, P. B., Perkins, K. K., Podolefsky, N. S., Reid, S., & LeMaster, R.
(2005). When learning about the real world is better done virtually: A study of substituting computer simulations for
laboratory equipment. Physical Review Physics Education Research, 1(1), 1.010103.
Frank, M., Bergelson, E., Bergmann, C., Cristia, A., Floccia, C., Gervain, J., LewWilliams, C., Nazzi, T., Panneton, R.,
Rabagliati, H., Soderstrom, M., Sullivan, J., Waxman, S., & Yurovsky, D. (2017). A collaborative approach to infant
research: Promoting reproducibility, best practices, and theorybuilding. Infancy, 22(4), 421435.
Fryer, R. G., Jr. (2011). Financial incentives and student achievement: Evidence from randomized trials. The Quarterly
Journal of Economics, 126(4), 17551798.
Fyfe, E. (2016). Providing feedback on computer-based algebra homework in middle-school classrooms. Computers in
Human Behavior, 63, 568574.
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1),
Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2016). Learning analytics should not promote one size fits all: The
effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28(1), 6884.
Goldstone, R. L., Kersten, A., & Carvalho, P. F. (2017). Categorization and Concepts. In J. Wixted (Ed.) Stevens’ Handbook
of Experimental Psychology and Cognitive Neuroscience, 4th ed., Volume Three: Language & Thought (pp. 275317).
New Jersey: Wiley.
Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012). The student activity meter for awareness and self-reflection.
CHI12 Extended Abstracts on Human Factors in Computing Systems (pp. 869884). New York: ACM.
Greeno, J. G., & Middle School Mathematics through Applications Project Group. (1998). The situativity of knowing,
learning, and research. American Psychologist, 53(1), 526.
Guskey, T., & Huberman, M. (1995). Professional development in education: New paradigms and practices. New York:
Teachers College Press.
Hall, G. (1891). The contents of childrens minds on entering school. The Pedagogical Seminary, 1(2), 139173.
Heath, L., Kendzierski, D., & Borgida, E. (1982). Evaluation of social programs: A multimethodological approach
combining a delayed treatment true experiment and multiple time series. Evaluation Review, 6(2), 233246.
Heffernan, N., & Heffernan, C. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers
together for minimally invasive research on human learning and teaching. International Journal of Artificial
Intelligence in Education, 24(4), 470497.
Henninger, F., Mertens, U. K., Shevchenko, Y., & Hillbig, B. E. (2017). lab.js: Browser-based behavioral research.
Higgins, M., Sävje, F., & Sekhon, J. (2016). Improving massive experiments with threshold blocking. Proceedings of the
National Academy of Sciences of the United States of America, 113(27), 73697376.
Imai, K., King, G., & Stuart, E. (2008). Misunderstandings between experimentalists and observationalists about causal
inference. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(2), 481502.
Khalil, M., & Ebner, M. (2015). Learning analytics: Principles and constraints. In S. Carliner & N. Ostashewski (Eds.),
Proceedings of the World Conference on Educational Media and Technology (EdMedia 2015), 2224 June 2015,
Montréal, Canada (pp. 17891799). Waynesville, NC: Association for the Advancement of Computing in Education
Klahr, D. & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction
and discovery learning. Psychological Science, 15(10), 661667.
Kirchoff, B. K., Delaney, P. F., Horton, M., & Dellinger-Johnston, R. (2014). Optimizing learning of scientific category
knowledge in the classroom: The case of plant identification. CBE Life Sciences Education, 13(3), 425436.
Kizilcec, R., & Brooks, C. (2017). Diverse big data and randomized field experiments in MOOCs. In C. Lang, G. Siemens,
A. Wise, and D. Gašević (Eds.), Handbook of Learning Analytics (pp. 211222). Society for Learning Analytics
Kizilcec, R., & Cohen, G. L. (2017). Eight-minute self-regulation intervention improves educational attainment at scale in
individualist but not collectivist cultures. Proceedings of the National Academy of Sciences of the United States of
America, 114(17), 43484353. 10.1073/pnas.1611898114
Kizilcec, R., Pérez-Sanagustín, M., & Maldonado, J. (2016). Recommending self-regulated learning strategies does not
improve performance in a MOOC. Proceedings of the 3rd ACM Conference on Learning @ Scale (L@S 2016), 2528
April 2016, Edinburgh, Scotland (pp. 101104). New York: ACM.
Klein, R., Ratliff, K., Vianello, M., Adams Jr., R., Bahník, Š., Bernstein, M., Bocian, K., … & Nosek, B. (2014).
Investigating variation in replicability. Social Psychology, 45, 142152.
Koedinger, K. R., Baker, R. S., Cunningham, K., Skogsholm, A., Leber, B., & Stamper, J. (2010). A data repository for the
EDM community: The PSLC DataShop. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. Baker, Handbook of
Educational Data Mining. Boca Raton, FL: CRC Press.
Koedinger, K., Booth, J., & Klahr, D. (2013a). Instructional complexity and the science to constrain it. Science, 342(6161),
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
Koedinger, K., Corbett, A., & Perfetti, C. (2012). The knowledgelearninginstruction framework: Bridging the science
practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757798.
Koedinger, K., & McLaughlin, E. (2016). Closing the loop with quantitative cognitive task analysis. In T. Barnes et al.
(Eds.), Proceedings of the 9th International Conference on Educational Data Mining (EDM2016), 29 June2 July
2016, Raleigh, NC, USA (pp. 412417). International Educational Data Mining Society.
Koedinger, K., Stamper, J., McLaughlin, E., & Nixon, T. (2013b). Using data-driven discovery of better student models to
improve student learning. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Proceedings of the 16th
International Conference on Artificial Intelligence in Education (AIED ʼ13), 9–13 July 2013, Memphis, TN, USA (pp.
421430). Springer.
Kolb, D. (1984). Experiential learning: Experience as the source of learning and development. Upper Saddle River, NJ:
Prentice Hall.
Krause, M. (2010). Undergraduates in the archives: Using an assessment rubric to measure learning. The American Archivist,
73, 507534.
Kumar, V., Clemens, C., & Harris, S. (2015). Causal models and big data learning analytics. In Kinshuk & R. Huang (Eds.),
Ubiquitous learning environments and technologies (pp. 3153). Springer.
Lader, E., Cannon, C., Ohman, E., Newby, L., Sulmasy, D., Barst, R., Fair, J., Flather, M., Freedman, J., Frye, R., Hand, M.,
Van de Werf, F., Costa, F., & American College of Cardiology Foundation (2004). The clinician as investigator:
Participating in clinical trials in the practice setting. Circulation, 109, 26722679.
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action aligning learning analytics with learning
design. American Behavioral Scientist, 57(10), 14391459.
Lodge, J., & Corrin, L. (2017). What data and analytics can and do say about effective learning. npj Science of Learning,
Macfadyen, L., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of
concept. Computers & Education, 54(2), 588599.
Maxwell, J. (2004). Causal explanation, qualitative research, and scientific inquiry in education. Educational Researcher,
33(2), 311.
Medin, D., & Bang, M. (2014). Whos asking? Native Science, Western Science and Science Education. Cambridge, MA:
MIT Press.
Moore, E. B., Herzog, T. A., & Perkins, K. K. (2013). Interactive simulations as implicit support for guided-inquiry.
Chemical Education Research and Practice, 14, 257268.
Morgan, K., & Rubin, D. (2012). Rerandomization to improve covariate balance in experiments. The Annals of Statistics,
40(2), 12631282.
Morrison, K., & van der Werf, G. (2016). Large-scale data, “wicked problems,” and “what works” for educational policy
making. Educational Research and Evaluation, 22(5/6), 255259.
Motz, B., Teague, J., & Shepard, L. (2015). Know thy students: Providing aggregate student data to instructors. EDUCAUSE
Review, 3.
Murnane, R., & Willett, J. (2010). Methods matter: Improving causal inference in educational and social science research.
New York: Oxford University Press.
National Research Council. (2012). Discipline-based education research: Understanding and improving learning in
undergraduate science and engineering. Washington, DC: National Academies Press.
Norris, D., Baer, L., Pugliese, L., & Lefrere, P. (2008). Action analytics: Measuring and improving performance that matters
in higher education. EDUCAUSE Review, 43(1), 4267.
Patsopoulos, N. (2011). A pragmatic view on pragmatic trials. Dialogues in Clinical Neuroscience, 13(2), 217224.
Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge, UK: Cambridge University Press.
Pearl, J., & Verma, T. (1995). A theory of inferred causation. Studies in Logic and the Foundations of Mathematics, 134,
Plaisant, C. (2004). The challenge of information visualization evaluation. Proceedings of the 2nd International Working
Conference on Advanced Visual Interfaces (AVI ’04), 25–28 May 2004, Gallipoli, Italy (pp. 109116). New York:
Rahman, S., Majumder, M., Shaban, S., Rahman, N., Ahmed, M., Abdulrahman, K. B., & DSouza, U. (2011). Physician
participation in clinical research and trials: Issues and approaches. Advances in Medical Education and Practice, 2,
ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported
(CC BY-NC-ND 3.0)
Reich, J. (2015). Rebooting MOOC research: Improve assessment, data sharing, and experimental design. Science
(Education Forum), 347(6217), 3435.
Renz, J., Hoffmann, D., Staubitz, T., & Meinel, C. (2016). Using A/B testing in MOOC environments. Proceedings of the 6th
International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp.
304313). New York: ACM.
Roediger, H. L., Agarwal, P. K., McDaniel, M. A., & McDermott, K. B. (2011). Test-enhanced learning in the classroom:
Long-term improvements from quizzing. Journal of Experimental Psychology: Applied, 17(4), 382395.
Russo, F. (2010). Causality and causal modeling in the social sciences. Springer.
Severance, C., Hanss, T., & Hardin, J. (2010). IMS learning tools interoperability: Enabling a mash-up approach to teaching
and learning tools. Technology, Instruction, Cognition, & Learning, 7, 245262.
Shadish, W., Campbell, D., & Cook, T. (2002). Experimental and quasi-experimental designs for generalized causal
inference. Boston, MA: Houghton Mifflin.
Siemens, G. (2012). Learning analytics: Envisioning a research discipline and a domain of practice. Proceedings of the 2nd
International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC,
Canada (pp. 48). New York: ACM.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 13801400.
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search. Cambridge, MA: MIT Press.
Stewart, N., Chandler, J., & Paolacci, G. (2017). Crowdsourcing samples in cognitive science. Trends in Cognitive Sciences,
21(10), 736748.
Sullivan, G. (2011). Getting off the gold standard: Randomized controlled trials and education research. Journal of
Graduate Medical Training, 3, 285289.
Tervakari, A., Silius, K., Koro, J., Paukkeri, J., & Pirttila, O. (2014). Usefulness of information visualizations based on
educational data. Proceedings of the 2014 Global Engineering Education Conference (EDUCON 2014), 35 April
2014, Istanbul, Turkey (pp. 142151). IEEE Computer Society.
Tufte, E. (2003). The cognitive style of PowerPoint. Cheshire, CT: Graphics Press.
US Department of Education. (2016). Using evidence to strengthen education investments (Non-regulatory guidance).
Washington, DC.
US Department of Education. (2017). What works clearinghouse standards handbook, Version 4.0. Washington, DC:
Institute of Education Sciences.
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. (2013). Learning analytics dashboard applications. American
Behavioral Scientist, 57(10), 15001509.
Wieman, C. E., Adams, W. K., & Perkins, K. K. (2008). PhET: Simulations that enhance learning. Science, 322(5902), 682
Williams, J., & Williams, B. (2013). Using randomized experiments as a methodological and conceptual tool for improving
the design of online learning environments.
Wise, A., & Shaffer, D. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics,
2(2), 513.
Zheng, Z., Vogelsang, T., & Pinkwart, N. (2015). The impact of small learning group composition on student engagement
and success in a MOOC. In O. C. Santos et al. (Eds.), Proceedings of the 8th International Conference on Educational
Data Mining (EDM2015), 2629 June 2015, Madrid, Spain (pp. 500503). International Educational Data Mining
... Embedded experimentation: Motz et al. highlighted the importance of scalable embedded experimentation in providing an accurate characterization of education in real-world scenarios, while also being able to make causal inferences. They also highlighted the necessity of working with instructors in the process of designing experimentation to further the applicability of results [24]. Our study addresses this framework through the deployment of A/B comparisons to isolate the effect of deliberate design factors. ...
... In terms of how useful the results were in testing predictions from instructors, we found that though instructors were generally able to predict the best condition to send in an email (though not always confidently), they would often overestimate the effectiveness of the emails when translated into student attempt rate and start times. This reinforces the notion that involving instructor intuition in the design process of reminder emails would allow for better conditions to be picked [24]. At the same time, this demonstrates the need for data from real-life A/B comparison experiments to help instructors quantify their intuitions and better understand the effectiveness of their communications. ...
Full-text available
Email communication between instructors and students is ubiquitous, and it could be valuable to explore ways of testing out how to make email messages more impactful. This paper explores the design space of using emails to get students to plan and reflect on starting weekly homework earlier. We deployed a series of email reminders using randomized A/B comparisons to test alternative factors in the design of these emails, providing examples of an experimental paradigm and metrics for a broader range of interventions. We also surveyed and interviewed instructors and students to compare their predictions about the effectiveness of the reminders with their actual impact. We present our results on which seemingly obvious predictions about effective emails are not borne out, despite there being evidence for further exploring these interventions, as they can sometimes motivate students to attempt their homework more often. We also present qualitative evidence about student opinions and behaviours after receiving the emails, to guide further interventions. These findings provide insight into how to use randomized A/B comparisons in everyday channels such as emails, to provide empirical evidence to test our beliefs about the effectiveness of alternative design choices.
... Thus, the privacy-preserved version obtained through our proposed techniques may be shared publicly to facilitate progress and reproducibility in scientific research related to EDM and LA. Additionally, our methods yielded small feature subsets; thus, after model building, the features' individual and collective (since they combine very intuitively) effects on the outcome variable can be examined easily by educational experts to understand their potential causal relevance, which is one of the key goals of LA [68]. Identifying a small feature subset also facilitates conducting controlled experiments through EdTechs to identify causal effects [68], since the number of variables to manipulate becomes much smaller than the full feature set. ...
... Additionally, our methods yielded small feature subsets; thus, after model building, the features' individual and collective (since they combine very intuitively) effects on the outcome variable can be examined easily by educational experts to understand their potential causal relevance, which is one of the key goals of LA [68]. Identifying a small feature subset also facilitates conducting controlled experiments through EdTechs to identify causal effects [68], since the number of variables to manipulate becomes much smaller than the full feature set. ...
Full-text available
Education technologies (EdTech) are becoming pervasive due to their cost-effectiveness, accessibility, and scalability. They also experienced accelerated market growth during the recent pandemic. EdTech collects massive amounts of students’ behavioral and (sensitive) demographic data, often justified by the potential to help students by personalizing education. Researchers voiced concerns regarding privacy and data abuses (e.g., targeted advertising) in the absence of clearly defined data collection and sharing policies. However, technical contributions to alleviating students’ privacy risks have been scarce. In this paper, we argue against collecting demographic data by showing that gender—a widely used demographic feature—does not causally affect students’ course performance: arguably the most popular target of predictive models. Then, we show that gender can be inferred from behavioral data; thus, simply leaving them out does not protect students’ privacy. Combining a feature selection mechanism with an adversarial censoring technique, we propose a novel approach to create a ‘private’ version of a dataset comprising of fewer features that predict the target without revealing the gender, and are interpretive. We conduct comprehensive experiments on a public dataset to demonstrate the robustness and generalizability of our mechanism.
... Most of the data in the field of learning analytics (LA) and educational data mining (EDM) are characterized by being big, second-hand, observational, and unstructured (Motz et al., 2018). 1 The data are big because they come from physical and virtual educational environments with many instructors and thousands of students and for whom several metrics exist (e.g., number of clicks, time stamps, course grades, etc.). The data are second-hand, observational, and unstructured because they are not obtained directly and the type and number of variables are not controlled by the researcher. ...
Full-text available
The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables. This article overviews the power and flexibility of GAMLSS in relation to some ML techniques. Also, GAMLSS' capability to be tailored toward causality via causal regularization is briefly commented. This overview is illustrated via a data set from the field of LA. This article is categorized under: Application Areas > Education and Learning Algorithmic Development > Statistics Technologies > Machine Learning
... Thus, the results do not further our understanding of whether or not light-touch interventions can foster virtue development. Furthermore, the fact that this intervention was not implemented in a highly controlled environment (i.e., laboratory setting), but instead was embedded in an authentic educational context (Motz, et al., 2018) speaks to the feasibility of efficacious undergraduate character education programs. ...
Full-text available
Intellectual virtues are gaining popularity in higher education as students, and citizens more broadly, face a torrent of (mis)information in the digital age. To foster the development of individuals with the attributes of careful, reflective thinking consonant with the ideals of liberal education, intellectual character education is being promoted. To date, the testing of this general theory has been limited to exploratory, pilot, or non-experimental evaluations. We report a multi-year experiment of a novel online, course-embedded intellectual virtue curriculum delivered in university classes that was designed to improve undergraduates' thinking dispositions. Within each course, we randomly assigned students (N = 361) to receive either the intellectual virtue curriculum or a control condition. College students in the intervention condition experienced a series of modules and activities related to developing four key thinking dispositions (curiosity, humility, integrity, and tenacity). We assessed students on these four dispositions and three self-reported knowledge of intellectual virtue measures at pretest and posttest. The analyses showed that the intervention has a positive impact on the intellectual virtue(s) of undergraduate students enrolled at university. Additionally, we find large effects of the intervention on self-reported knowledge measures. Results have implications for virtue science, character education programs, and studies related to improving critical thinking.
... Emerging work in causal inference suggests ways to develop intervention models from data as opposed to prediction models in various domains [e.g. 59,68], but existing causal inference approaches such as randomized controlled trials have known challenges with external validity [24,79]; as such, identifying actionable insights with causal methods remains an ongoing project. Moreover, Kohler et al. [46] and Hu et al. [39] have pointed out significant conceptual flaws with interpreting social categories such as race and gender as causally manipulable variables (such as in a causal diagram [65]), suggesting that the validity of causal inference cannot be taken for granted where demographic aspects of student data are concerned. ...
Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand. We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research, as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.
... This is increasingly recognized as a serious concern for traditional psychological experiments, which typically study only narrow and unrepresentative groups of people (Henrich et al., 2010), in narrowly constrained contexts (Baribault et al., 2018)-leading to a putative "generalizability crisis" (Hilton and Mehr, 2021;Yarkoni, 2021). These concerns have motivated the need to systematically embed educational experiments within real-world learning contexts (Motz et al., 2018;Fyfe et al., 2021) and for research teams to collaborate deeply with educators in both research and implementation (Penuel et al., 2011)-as we have done here. So while there are challenges in conducting research in real-world teaching contexts, there are potentially equally as many opportunities. ...
A critical goal for science education is to design and implement learning activities that develop a deep conceptual understanding, are engaging for students, and are scalable for large classes or those with few resources. Approaches based on peer learning and online technologies show promise for scalability but often lack a grounding in cognitive learning principles relating to conceptual understanding. Here, we present a novel design for combining these elements in a principled way. The design centers on having students author multiple-choice questions for their peers using the online platform PeerWise, where beneficial forms of cognitive engagement are encouraged via a series of supporting activities. We evaluated an implementation of this design within a cohort of 632 students in an undergraduate biochemistry course. Our results show a robust relationship between the quality of question authoring and relevant learning outcomes, even after controlling for the confounding influence of prior grades. We conclude by discussing practical and theoretical implications.
... Unfortunately, we think that this description is more about our aspirations than our reality. While researchers now routinely run learning experiments in live classes (Motz, Carvalho, de Leeuw, & Goldstone, 2018), the predictions inferred from these studies are almost never informed by moderating variables (Koedinger, Booth, & Klahr, 2013). Studies conducted in small numbers of classes are commonly assumed to apply to all classes. ...
Emphasizing the predictive success and practical utility of psychological science is an admirable goal but it will require a substantive shift in how we design research. Applied research often assumes that findings are transferable to all practices, insensitive to variation between implementations. We describe efforts to quantify and close this practice-to-practice gap in education research.
... Unfortunately, we think that this description is more about our aspirations than our reality. While researchers now routinely run learning experiments in live classes (Motz, Carvalho, de Leeuw, & Goldstone, 2018), the predictions inferred from these studies are almost never informed by moderating variables (Koedinger, Booth, & Klahr, 2013). Studies conducted in small numbers of classes are commonly assumed to apply to all classes. ...
Yarkoni's analysis clearly articulates a number of concerns limiting the generalizability and explanatory power of psychological findings, many of which are compounded in infancy research. ManyBabies addresses these concerns via a radically collaborative, large-scale and open approach to research that is grounded in theory-building, committed to diversification, and focused on understanding sources of variation.
... Beyond this, A/B testing and other forms of rapid automated or semi-automated experimentation (see review in Motz et al., 2018) make it possible to quickly ask questions about learning. Currently, this type of technology is only used within a small number of platforms and studies (see review in Savi et al., 2017), although its scope is expanding over time. ...
Full-text available
Although the idea of learning engineering dates back to the 1960s, there has been an explosion of interest in the area in the last decade. This interest has been driven by an expansion in the computational methods available both for scaled data analysis and for much faster experimentation and iteration on student learning experiences. This article describes the findings of a virtual convening brought together to discuss the potential of learning engineering and the key opportunities available for learning engineering over the next decades. We focus the many possibilities into ten key opportunities for the field, which in turn group into three broad areas of opportunity. We discuss the state of the current art in these ten opportunities and key points of leverage. In these cases, a relatively modest shift in the field's priorities and work may have an outsized impact.
Full-text available
This article sets out to explore a shift in the sources of evidence-of-learning in the era of networked computing. One of the key features of recent developments has been popularly characterized as ‘big data’. We begin by examining, in general terms, the frame of reference of contemporary debates on machine intelligence and the role of machines in supporting and extending human intelligence. We go on to explore three kinds of application of computers to the task of providing evidence-of-learning to students and teachers: (1) the mechanization of tests—for instance, computer adaptive testing, and automated essay grading; (2) data mining of unstructured data—for instance, the texts of student interaction with digital artifacts, textual interactions with each other, and body sensors; (3) the design and analysis of mechanisms for the collection and analysis of structured data embedded within the learning process—for instance, in learning management systems, intelligent tutors, and simulations. A consequence of each and all of these developments is the potential to record and analyze the ‘big data’ that is generated. The article presents both an optimistic view of what may be possible as these technologies and pedagogies evolve, while offering cautionary warnings about associated dangers.
Full-text available
Crowdsourcing data collection from research participants recruited from online labor markets is now common in cognitive science. We review who is in the crowd and who can be reached by the average laboratory. We discuss reproducibility and review some recent methodological innovations for online experiments. We consider the design of research studies and arising ethical issues. We review how to code experiments for the web, what is known about video and audio presentation, and the measurement of reaction times. We close with comments about the high levels of experience of many participants and an emerging tragedy of the commons.
Full-text available
Students in the United States consistently underperform on state tests of mathe- matical pro ciency (e.g., Kim, Schneider, Engec, & Siskind, 2006; Pennsylvania Department of Education, 2011) and in international comparisons on the Trends in International Mathematics and Science Study (TIMSS; e.g., Mullis, Martin, Foy, & Arora, 2012) and Programme for International Student Assessment (PISA; e.g., Fleischman, Hopstock, Pelczar, & Shelley, 2010). Among other issues, aspects of early mathematics instruction can interfere with later learn- ing (McNeil et al., 2006; see also McNeil et al., Chapter 8; Van Hoof et al., Chapter 5) and students are not adequately prepared to tackle dif cult gate- keepers, such as fractions (Booth & Newton, 2012) and algebra (Department of Education, 1997), that in turn prevent them from advancing in the elds of Science, Technology, Engineering, and Mathematics (STEM). To address these issues, educators and researchers have repeatedly called for mathematics instruction in the United States to be based on evidence (e.g., CCSSI, 2010; NCLB, 2002; NMAP, 2008).Over the past few decades, the eld of cognitive science has identi ed fac- tors that have the potential to substantively improve students’ learning. These principles have been detailed in several reviews (e.g., Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013; Koedinger, Booth, & Klahr, 2013; Pashler et al., 2007), and many involve a comparison of different approaches (such as spaced vs. massed practice), showing that one type of instructional technique is superior to another type of instructional technique (Koedinger et al., 2013). Many of these cognitive science principles are potentially useful for improving mathematics instruction, perhaps especially those concerning abstract and con- crete representations, analogical comparison, feedback, error re ection, scaf- folding, distributed practice, interleaved practice, and worked examples.
Recently, interest in how this data can be used to improve teaching and learning has also seen unprecedented growth and the emergence of the field of learning analytics. In other fields, analytics tools already enable the statistical evaluation of rich data sources and the identification of patterns within the data. These patterns are then used to better predict future events and make informed decisions aimed at improving outcomes (Educause, 2010). This paper reviews the literature related to this emerging field and seeks to define learning analytics, its processes, and its potential to advance teaching and learning in online education.
The building blocks of human cognition are concepts. What we see, hear, interpret, remember, understand, and talk about is crucially shaped by our concepts. They allow us to communicate, categorize objects and events into inductively powerful groups, organize our world, construct complicated thoughts, and conserve memory resources. Alternative theories have proposed that concepts are represented by rules, prototypical category members, many specific exemplars, and structured theories. We consider empirical evidence bearing on these proposals, which, on balance, recommends pluralism, with different representations implicated in different situations. We consider ways in which concepts shape, and are shaped by, perception and language. Concept‐learning research has important educational applications for helping students learn concepts in an efficient and generalizable manner, and we review what is known about how best to improve concept‐learning outcomes. Finally, we consider future directions for research on concepts and categorization, emphasizing links to other fields, such as object recognition and developmental psychology, and the development and testing of computational models.
The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, not of regularity nor invariance, thus breaking down the dominant Humean paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models: e.g. Rubin’s model, contingency tables, and multilevel analysis. It is also shown to be latent—yet fundamental—in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science. "Dr. Federica Russo's book is a very valuable addition to a small number of relevant publications on causality and causal modelling in the social sciences viewed from a philosophical approach".(Prof. Guillaume Wunsch, Institute of Demography, University of Louvain, Belgium)
Writing is often used as a tool for learning. However, empirical support for the benefits of writing-to-learn is mixed, likely because the literature conflates diverse activities (e.g., summaries, term papers) under the single umbrella of writing-to-learn. Following recent trends in the writing-to-learn literature, the authors focus on the underlying cognitive processes. They draw on the largely independent writing-to-learn and cognitive psychology learning literatures to identify important cognitive processes. The current experiment examines learning from 3 writing tasks (and 1 nonwriting control), with an emphasis on whether or not the tasks engaged retrieval. Tasks that engaged retrieval (essay writing and free recall) led to better final test performance than those that did not (note taking and highlighting). Individual differences in structure building (the ability to construct mental representations of narratives; Gernsbacher, Varner, & Faust, 1990) modified this effect; skilled structure builders benefited more from essay writing and free recall than did less skilled structure builders. Further, more essay-like responses led to better performance, implicating the importance of additional cognitive processes such as reorganization and elaboration. The results highlight how both task instructions and individual differences affect the cognitive processes involved when writing-to-learn, with consequences for the effectiveness of the learning strategy. (PsycINFO Database Record
Significance High attrition from educational programs is a major obstacle to social mobility and a persistent source of economic inefficiency. Over two-thirds of students entering a 2-y institution fail to earn a credential in the United States. In online courses, attrition rates are even higher. In two large field experiments, we tested the conditions under which a writing activity that facilitates goal commitment and goal-directed behavior reduces attrition in online courses. The activity raised completion rates by up to 78% for members of individualist cultures and primarily for those who contended with predictable and surmountable obstacles in the form of everyday obligations, but it was ineffective in collectivist cultures and for people contending with other types of obstacles.