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Shawn Allen

English 102

Professor Burke

29Nov2015

Survey of Learning Methods: A Neuroscience Neophytes Guide to Learning More In Less Time.

It is not without great irony that many people spend at least 13 years in formalized

education, some upwards of 20 years, and most of whom do not think about learning how to

learn effectively – let alone what is on the bleeding edge of learning methods, techniques &

theories. Everybody learns differently. While this familiar adage is true, the rabbit hole of

learning is so much deeper than the proverb credits it. Not only does everybody learn differently,

but everyone learns different types of subject matter differently and across different learning

methodologies – differently. Some methods that would be advantageous to employ learning a

language, for instance, would not be so advantageous for logically figuring out a problem in a

chemistry class. Students often do not know how to study in such a way that will maximize their

study time effectively, and similarly, teachers might not be trained in the appropriate areas to

maximize learning outcomes (Dunlosky et al). It would be truly surprising if any student could

recall their time in formalized education without recalling the techniques of summarization,

highlighting, rereading, or notetaking. It may or may not be surprising to find out these

techniques are not very effective (Dunlosky et al). This of course begs the question: what are the

most effective learning strategies for X use case? In an attempt some of this information

accessible to those of whom are not neuroscience or learning psychology majors, this paper will

look at various research studies that discuss: techniques students report using most often that are

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not successful; classical learning methodologies that have demonstrated the most consistent

effects across a wide range of use cases and learning environments; and finally, a close up look

at the merits of a new fad on the bleeding edge in e-learning – the spaced learning effect.

There a few things to note about interpreting the results of this paper, and many of the

studies reviewed This is especially true of those reviewed by Dunlosky et al, which seemed to

be the seminal work on collecting and correlating a lot of these subjects at the time of this

writing. Most of the studies are aimed at wide implementation and evaluate the subsequent

methods through that lens. The Dunlosky team also looked at most of the subjects with studies

that accounted for intellectual differences, like functional memory, reading ability, and prior

knowledge; as well as learning environment differences, implementation difficulties, and other

such metrics. Many of these types of studies are left out in favor of more interesting and broadly

applicable ones, but is an area left for one to consider for further reading. Some of the more

exotic methods have more narrow use cases, and although promising perhaps, are the least

researched. A lot of the research conflicts. So in light of that fact, many methods herein are

discussed in a simple pro versus con context, while attempting to let one decide which methods

may be appealing, but ultimately staying in line with what seems to be the most reputable

research. So when one evaluates the information herein: do so acknowledging that nothing is

concrete, everything is a spectrum, most everything is likely under-researched in some aspect or

many, and again, everybody learns differently to an extent.

In order to establish a baseline for what most people experience in formalized western

education one could easily argue the most ubiquitous would be the summary. Unlike some of the

other study methods, summaries are in fact well researched. In terms of frequency, as noted

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above, in many students learning experiences summaries have been used quite extensively, but is

that usage justified?

On the positive side of things, “Thiede and Anderson (2003) had students read expository

texts over a variety of topics. In one condition, students were told to make a metacomprehension

judgment for each text while the other condition was asked to generate a summary of the text

prior to completing the judgment. They observed that the students who generated the summaries

had higher judgment accuracy than when they did not” (qtd. in Poulin). In a study from Bretzing

and Kulhavy from 1979 which tested summarizers vs. note takers vs. other methods like

verbatim copying, they found summarizers got scores that about equaled note-takers, receiving

scores of 14 and 15 out of 25 respectively on an immediate test, and 10 and 9 respectively out of

25 on a delayed test (qtd. in Dunlosky et al). Upon reading this one should immediately notice

the lack of retention from the immediate test to the delayed test. Another one of the biggest

takeaways from these results is that summarizing and note-taking were more effective than

verbatim copying. The rationale behind the line of thinking here is that: formulating the subject

matter into ones’ own words, or synthesizing, especially when selecting the most important

information, increases retention due to this synthesis (Dunlosky et al).

Despite this reported upside of increased metacomprehension, and slightly increased

retention over verbatim copying, there is not much more strong evidence to support using

summaries. Poulin, in her master’s thesis, observes that “using summarization to improve

[metacomprehension] accuracy has its limitations”. Poulin also states the primary determinant

factor and implementation issue of summarizing is the student must be trained to summarize

correctly – differentiating between a strong and a weak summary; or put another way, one that

really pulls out the main ideas and only the main ideas of the passages. Poulin goes onto

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conclude: “while summarization might be beneficial to structuring the content during study, it

overlooks the research that has consistently shown that students are highly overconfident in their

future academic success prior to and after reading educational materials.” Overall, Dunlosky and

his team rate it as a “low-utility” activity, which, is a relative score, meaning that among the ten

methods they choose to investigate in that paper, this one was not worth the time it in most use

cases and learning environments relative to the other methods available to the learner.

Highlighting is yet another familiar memory of formalized education. Similar in many

aspects to notetaking and summarizing, but primarily, one is asking the reader to take only the

best ideas from the subject matter and highlight or underline them. It is such common a practice

that indeed it could be said that “any educator who has examined students’ course materials is

familiar with the sight of a marked-up, multicolored textbook.” (Dunlosky et al). (Dunlosky goes

on to mention there are various studies confirming its popularity.) So it is because of the

popularity that highlighting seems to enjoy that the topic is mentioned here – mostly as a

warning. The best thing about highlighting is it takes relatively little effort, unfortunately, the

long-term educational benefit is fairly low as well. In the studies Dunlosky et al looked at, there

was relatively nothing substantially positive about the effects of highlighting, and again was only

when it was implemented correctly. Specifically, highlighting only the main ideas and nothing

else. Again, Dunlosky and team rate this activity as “Low Utility”, partly due to implementation

difficulties (i.e. doing it quickly and correctly), and partly due to it not having an empirically

proven educational benefit.

Rereading is a bit more of a controversial topic, so ones’ own mileage may vary here. At

a quick glance, rereading does seem to have at least some retention benefits in various studies.

Moreover, in a study conducted by Peter Verkoeijen and his team, the observation was made that

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there might be a correlation between rereading and the timing of that rereading session for

maximum long term retention benefits.

This next study on rereading gets a bit into spaced learning, which will be discussed in

more depth in the next section, but the basic tenet that one needs to understand is that you have a

space in between learning sessions, and furthermore, that you carefully considering the timing or

spaces between these sessions depending on one’s desired learning outcome. In the study, which

used about 60 psychology undergrads, “participants read the text twice, either in immediate

succession (massed repetition), with a 4-day interstudy interval (spaced short), or with a 3.5-

weeks interstudy interval (spaced long). Two days after the second study trial, all participants

were tested” (Verkoeijen et al). The results of this test are quite curious indeed – finding that the

participants of the first group and the third group scored about the same. The group in the

middle, the group that had the 4 day interval between study sessions: had apparently found the

rereading goldilocks zone, with their scores significantly outmatching the other two groups with

a mean of about 42 versus 60 (Verkoeijen et al). Ultimately, the most important takeaway is that

you can actually hurt retention by rereading too soon.

Another study on rereading conducted by Rothkoph in 1968 found that there is seemingly

no difference between rereading the subject matter 3 and 4 times, but it did show there was a

mean difference of about 8 percentage points in favor of those who read twice as opposed to

those who read once (qtd. in Dunlosky et al). Dunlosky and team conclude rereading can create

durable memories they qualify that by saying “most effects have been shown with recall-based

memory measures, whereas the benefit for comprehension is less clear.”This earns the activity a

Dunlosky chart: “Low Utility”. Although it has been demonstrated that rereading has some

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merit in durable learning, they deem it this way relative to the other methods available to the

learner.

The techniques reviewed thus far have represented some of the common experiences that

students self-report using the most often, but it is a wonder why they do. As one can see from the

studies reviewed so far, none of these techniques are especially effective, yet they keep being

employed by students and educators again and again. Luckily, and apparently contrary to popular

belief, these are not the only tools available. The focus will now shift on the most proactive ways

a prospective student can spend their study time. Some of these ideas are not exactly new, but, as

per usual, the devil lies in the details of implementation.

Elaborative interrogation is a term perhaps not many are familiar with, but it basically

boils down to asking: Why? There are various situations in which one might be engaged with

elaborative interrogation, it may in response to a prompt from a professor, or a prompt on a

screen from a computer. Perhaps the most common form of elaborative interrogation is self-

explanation (Dulosky et al). (Although Dunlosky differentiates the two concepts slightly.) The

next study is one of the earliest studies of elaborative interrogation:

Pressley, McDaniel, Turnure, Wood, and Ahmad (1987) presented undergraduate

students with a list of sentences, each describing the action of a particular man

(e.g., “The hungry man got into the car”). In the elaborative-interrogation group,

for each sentence, participants were prompted to explain “Why did that particular

man do that?” Another group of participants was instead provided with an

explanation for each sentence (e.g., “The hungry man got into the car to go to the

restaurant”), and a third group simply read each sentence. On a final test in which

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participants were cued to recall which man performed each action (e.g., “Who got

in the car?”), the elaborative-interrogation group substantially outperformed the

other two groups (collapsing across experiments, accuracy in this group was

approximately 72%, compared with approximately 37% in each of the other two

groups). From this and similar studies, Seifert (1993) reported average effect sizes

ranging from 0.85 to 2.57 (qtd. in Dunlosky et al).

These studies are profound not only because of the results, but because of the multitude

of studies present confirming them. Elaborative interrogation has proven not only to be effective

with these “Man does X, for Y reason” questions, but also for more classical material (Dunlosky

et al). Overall, it is rated by Dunlosky and team as an activity with “moderate utility”. Dunlosky

and team came to this conclusion because of this methods relatively proven track record with

fact based material, however, they feel that it does not have the same track record with more

complex and lengthy material, and that it also requires more study about retention time,

otherwise, they might have rated it as “high utility”.

Another method that has been proven to work fairly evenly across all the different

learning metrics and environments is practice testing. Distinct from actual testing, in the sense

that it is a ‘low stakes’ type of test where the focus for the student really becomes about

metacomprehension. There is a wide definition as well, Dunlosky et al defines its’ scope as

“practicing recall of target information via the use of actual or virtual flashcards, completing

practice problems or questions included at the end of textbook chapters, or completing practice

tests included in the electronic supplemental materials that increasingly accompany textbooks.”

There is quite a bit of connective tissue between how spaced learning and practice testing are

studied, and a lot of studies that focus on the “testing effect”, (similar phrasing and concept as

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spacing effect), are dealing with the time spaced between these practice testing sessions and the

immediacy of feedback.

In a follow up to a call for more study from Dunlosky and Rawson a newer study on the

subject of practice testing from 2014, which paid special attention to accounting for working

memory capacity, the importance of immediate and correct feedback, as well as various other

factors that were considered due to prior research done on the subject. The setup for the study

conducted by Wiklund-Hörnqvist et al repeated a Rawson & Dunlosky 2011 experiment:

Following these common learning phases (lecture and familiarization), the two

groups were assigned to different experimental procedures namely, either

rereading the facts (restudy group; SS) or taking a test with feedback (test group;

STfb) six consecutive times. For the SS-group, a key-concept was presented (15

sec.), and the instruction only required subjects to study For the STfb-group, a

key-concept leaving out the keyword was presented (15 sec.) and subjects were

requested to type in the correct answer at a blank screen (10 sec). This was

followed by feedback in the form of the correct answer (5 sec) (Wiklund-

Hörnqvist et al).

The mean proportion of correct responses for the STtb and SS group for three time points

respectively were: ~83 vs ~70 on an immediate test, ~58 vs ~45 on a 18-day delay test – with no

significant difference from then till the 5-week delay test (Wiklund-Hörnqvis et al). The results

of this particular study are fairly striking with the obvious gap between the two groups, some in

the practice group even scored nearly 100% on the exam. One of the interesting inferences from

this data set is that the retention of the information is encoded into long term memory at a

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significantly higher rate as well. Dunlosky et al rated it as having “High Utility”, concluding it

had low implementation difficulties, and high rewards relative to other methods.

Spaced learning or otherwise known as the retrieval spacing effect, has become

reasonably popular with those learning foreign languages and other fact based study online in the

form of a software which are known as: spaced repetition systems. This is a general category of

software comprised of: a media-rich flashcard system; the ability to get immediate correct

answer feedback, and the ability to have one be able to report metacomprehension by labeling

the cards easy, difficult or somewhere between, which in turn sets the spacing of the card to a

user defined long or short time-period for review. It seems it is not just our homo-sapien brains

that are structured in a fashion that makes correlations upon spaced retrieval cues in memory as:

“recently a robust model of LTM [Long-Term Memory] formation has emerged through studies

of late Long-Term Potentiation (LTP) and LTM in many different contexts and species” (qtd. in

Kelly & Whatson).

Demonstrating this effect with the unlikely candidate of honeybees Menzel et al., 2001

reported: “using spaces between stimuli of 30 sec, 3 min, and 10 min, memory retention was

tested after 30 min, one day, and 3 days. Honeybees trained with 30 sec spaces showed the best

learning after 30 min with over 80% retention, but this rapidly decreased, falling to 20% on the

third day, a demonstration only STM had been created. In contrast, honeybees trained with 10

min spaces between learning trials showed less than 80% retention after 30 min but subsequently

consolidated these memories, reaching almost 100% on the third day, demonstrating [LTM] had

been created” (qtd. in Kelly and Whatson). One of the problems with the research so far had

been the lack of studies focused on high stakes examination outcomes. In the study conducted by

Kelly and Whatson they looked at exactly this, using a method they “derived directly from the

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research demonstrating LTM mechanisms of DNA synthesis at an intracellular level can be

triggered using three stimuli spaced by two 10 min periods without stimulation” they went on to

craft a way to apply these constraints in a learning environment:

A Technique was developed based on this pattern to test weather encoding

complex information into LTM in students was possible using the pattern within a

very short time scale. In an educational context, stimuli were periods of highly

compressed instruction, and spaces were created through 10 min distractor

activities. Spaced Learning in this form was used only as the means of instruction

for a national curriculum of biology courses. Remarkably, learning at a greatly

increased speed and in a pattern that included deliberate distraction produced

significantly highly scores than random answers (p < 0.00001) and scores were

not significantly different for experimental groups (one hour spaced learning) and

control groups (four months teaching). Thus learning per hour of instruction, as

measured by the test, was significantly for the spaced learning groups (p <

0.00001). In a third condition, spaced learning was used to replace the end of

course review for one of two examinations. Results showed significantly higher

outcomes for the course using spaced learning (p < 0.0005) (Kelly and Whatson).

It is truly amazing that the experimental group nearly matched the 4-month classical

education group and that the spaced review group reported significantly increased scores over

the classical review group (Kelly and Whatson). Drawing conclusions from these two points:

perhaps some of the highest utility might be in a hybridization of classical education and the

tenets of spaced learning. This particular method does not get a mention in Dunlosky’s chart,

however, Dunlosky and Rawson have done some research on the effect in other papers, which is

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definitely an area suggested for further reading. In rating this methods utility and staying

consistent with the rest of the previous analysis, one may look to the components this method is

essentially comprised of: spaced sessions of practice testing with immediate feedback and focus

on metacomprehension. After looking at it from such a vantage, it is relatively reasonable to

conclude that such systems are of high-utility, and in fact, hybridize most of the strong points of

all the methodologies discussed so far.  

Yes, everyone does learn a bit differently, but, as has been demonstrated, there is much

commonality to learning as well, even to very distantly related species, like honeybees. Spaced

Learning, like many of these methods discussed herein, is not a very new idea. However, despite

the copious amount of research documenting the existence of the effect, the overall impact into

formalized education has been minimal at best (Kelly and Whatson). Techniques like

highlighting, summarizing, and note-taking still describe the majority of student experiences.

One could easily say that awareness is actually the primary implementation problem for the high-

utility methods, and also the reason why lower-utility methods are not more readily abandoned.

In all likelihood, it will take an entirely new generation of teachers to really shift the paradigm of

twenty-first education into a direction that is backed by neurologists, until then, one can always

benefit from analyzing the ways in which one learns best individually, and supplement one’s

own curriculum with some of methods that were discussed here. It is an exciting time for

neuroscience, the science how we learn is still in its infancy with much room for improvement

and further research. Suggestions for further reading include: anything written by John Dunlosky

and Katherine Rawson, as well as the work from Kelly and Whatson listed in the works cited.

.

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Works Cited

Dunlosky, J., K. A. Rawson, E. J. Marsh, M. J. Nathan, and D. T. Willingham.

"Improving Students' Learning with Effective Learning Techniques: Promising

Directions from Cognitive and Educational Psychology." Psychological Science

in the Public Interest 14.1 (2013): 4-58. Indiana University, 2013. Web. 29 Nov.

2015.

Kelly, Paul, and Terry Whatson. "Making Long-term Memories in Minutes: A Spaced

Learning Pattern from Memory Research in Education." Frontiers. Frontiers in

Human Neuroscience, 25 Sept. 2013. Web. 29 Nov. 2015.

<http://dx.doi.org/10.3389/fnhum.2013.00589>.

Poulin, Christina Marie. "Do Rubrics Improve Students’ Metacomprehension Accuracy?"

Thesis. Kent State, 2013. Https://etd.ohiolink.edu/!etd.send_file?

accession=kent1374595640&disposition=inline. Kent State. Web.

<https://etd.ohiolink.edu/!etd.send_file?

accession=kent1374595640&disposition=inline>.

Rawson, Katherine A., John Dunlosky, and Sharon M. Sciartelli. "The Power of

Successive Relearning: Improving Performance on Course Exams and Long-Term

Retention." Educational Psychology Review Educ Psychol Rev 25.4 (2013): 523-

48. Web.

Verkoeijen, Peter P. J. L., Remy M. J. P. Rikers, and Binnur Özsoy. "Distributed

Rereading Can Hurt the Spacing Effect in Text Memory." Applied Cognitive

Psychology Appl. Cognit. Psychol. 22.5 (2008): 685-95. Web.

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Wiklund-Hörnqvist, Carola, Bert Jonsson, and Lars Nyberg. "Strengthening Concept

Learning by Repeated Testing." Scandinavian Journal of Psychology Scand J

Psychol 55.1 (2013): 10-16. Web.