The pen is mightier than the keyboard: advantages of longhand over laptop note taking

Mueller, P. A., & Oppenheimer, D. M. (2014). The pen is mightier than the keyboard: advantages of longhand over laptop note taking. Psychological science, 0956797614524581.‏ http://drawingchildrenintoreading.com/assets/the_pen_is_mightier_than_the_keyboard-libre.pdf

 

Some of you are probably familiar with that moment in a lecture when the speaker promises to "email the presentation" but some people continue to take notes by hand.  When asked, they usually say things like: "writing helps me learn/understand better"; "I understand it better through my hand" etc.

 

Is it really so?

 

There is a substantial literature on the general effectiveness of note taking in educational settings, but it mostly predates laptop use in classrooms. Research shows that processing which takes place during note taking improves learning and recall of the material.  The existence of the notes enables people to review the material, an action that strengthens learning further.

 

Note taking can be generative (e.g., summarizing, paraphrasing, concept mapping) or nongenerative (i.e., verbatim copying). Verbatim note taking has generally been seen to indicate relatively shallow cognitive processing.  The more deeply information is processed during note taking, the greater the encoding benefits. Verbatim note taking predicts poorer performance than nonverbatim note taking, especially on integrative and conceptual items.

 

Laptop use facilitates verbatim transcription of lecture content because most students can type significantly faster than they can write (this is probably not true for school children, except maybe in high school).  The slower rate of manual note taking forces the writer to discriminate between what's important and what's less important, and to rephrase the main ideas rather than to write verbatim.

 

In order to find out how manual note taking influences learning and understanding of lectures (versus laptop note taking), Mueller& Oppenheimer conducted three experiments.

 

Sixty five students from Princeton University participated in the first study. They watched TED talks, each of them about 15 minutes long.  The researchers chose   topics that were interesting but not common knowledge.

Participants were instructed to use their normal classroom note-taking strategy.  Half of the students took note by hand and half by laptop. Then the students performed distraction tasks for 30 minutes, after which they answered factual-recall questions  and conceptual-application questions  about the lecture.  On factual-recall questions, participants performed equally well across conditions (manual vs. laptop note taking). However, on conceptual-application questions, laptop participants performed significantly worse than longhand participants.

 

There were several qualitative differences between laptop and longhand notes.  Participants who took longhand notes wrote significantly fewer words   than those who typed.   Laptop notes contained an average of 14.6% verbatim overlap with the lecture, whereas longhand notes averaged only 8.8%.  This difference was significant, and it means that the longhand notes reflected deeper processing of the material than the laptop notes.

 

In the second experiment, participants were 151 students from the University of California.  In this experiment, the researchers asked a subgroup of laptop note takers to refrain from verbatim note taking ("take notes in your own words and don’t just write down word-for-word what the speaker is saying”).  They wanted to know whether this instruction will affect the student's success in the test.  Another group of students took longhand notes and a third group took laptop notes without instruction to write in their own words.

 

In this experiment, too, students who took manual notes performed better on conceptual-application questions than students who took notes on a laptop.    The instruction to not take verbatim notes was completely ineffective at reducing verbatim content.    The   verbatim overlap of laptop-nonintervention participants  was 12.11%, and  of  laptop-intervention participants was 12.07%.

 

In the third experiment, the researchers wanted to see if the note's length influences the quality of learning when participants get a chance to review the material prior to being tested.   Participants were 109 students from the University of California.  They watched four seven minute lectures and took notes on a laptop or by hand.  They were told they would be returning the following week to be tested on the material.   When participants returned, those in the study condition were given 10 min to study their notes before being tested. Participants in the no-study condition immediately took the test.

Participants who took longhand notes and were able to study them performed significantly better than participants in any of the other conditions (handwritten notes and no review, laptop notes and no review, laptop notes and review).  When participants were unable to study, there was no difference between laptop and longhand note taking

 

To summarize, the combination of handwritten notes and reviewing the material lead to the best learning.  The reason for that could be the deeper processing that takes place when people take notes by hand.  This deeper processing leads to better, more conceptualized notes.  Reviewing such notes is more efficient.  Although manual notes are shorter than laptop notes, they are also more conceptualized and integrative and lead to better learning.

 

I suppose these findings are relevant mostly to high school students, since they are more likely than younger children to type automatically and faster than they write.  Nevertheless, these findings illuminate the importance of manual note taking to the learning process.

 

 

The Interleaving Effect: Mixing It Up Boosts Learning

The Interleaving Effect:

 Mixing It Up Boosts Learning

Studying related skills or concepts in parallel is a surprisingly effective way to train your brain

By Steven C. Pan on August 4, 2015

Scientific American

What makes learning, storage and retrieval more efficient?

Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417-444.‏ http://cognitrn.psych.indiana.edu/rgoldsto/courses/cogscilearning/bjorkdunlosky.pdf

Here are insights from this interesting paper (with a few additions by me):

We do not store information in our long-term memories by making any kind of literal recording of that information, but, instead, we do so by relating new information to what we already know. We store new information in terms of its meaning to us, as defined by its relationships and semantic associations to information that already exists in our memories.  Storing information in human memory appears to create capacity—that is, opportunities for additional linkages and storage—rather than use it up.   I'll add, that when a piece of information is tied to a broader net of ideas and concepts, it is learned better.  This is why people with a broad knowledge base and good fluid abilities (that help them conceptualize links between new and old pieces of information), have an advantage in learning.

When we retrieve information from memory, we do not retrieve an exact "recording" of this information as it existed when it was learned.  We re-edit the memory.  When we recall an event, for example, we integrate features of the event with our assumptions, goals or experiences.  I'll add, that the manipulations we perform on the information we retrieve, like telling it or writing it down, also cause it to be re-edited and then later re-stored in memory as slightly different.  The context in which we retrieve the information also changes it and adds aspects that were not in the information at the time it was first stored.

The mere act of retrieval strengthens the information retrieved, so it becomes easier to retrieve it later.  Thus, when we study for a test, it's better to test ourselves and to retrieve the information from memory than to re-read it time and again.  The mere act of retrieval of specific information weakens our ability to retrieve similar competing information, which is tied to the same retrieval cues or semantic associations.  This phenomenon is called "retrieval induced forgetting".

We learn and retrieve information better when we introduce difficulties and challenges to the learning process.  Self testing (as opposed to passive rehearsals of the information) is an example of such a "desirable difficulty".  Self testing not only makes sure that we studied the information.  It is a part of the learning process itself.  When we answer a question on a self test, we reorganize the material and elaborate it.

Meta-cognitive monitoring and control are important parts of the leaning process.  During learning we evaluate the situation and make decisions, like what to learn next, how to learn it, whether we've learned enough for the information to be retrieved, whether we retrieved the information correctly, and so on.

It's recommended to space learning sessions across days, rather than to cram it in one session.  It's better to study two or three subjects simultaneously and to alternate between them ("interleaving"), than to study only one subject in depth.  When we study one subject in depth, processing is easier and more fluent than when we alternate between two subjects .  This is because when we massively study one subject, we hold the same concepts in working memory.  When we alternate between subjects, we need to re-retrieve the concepts of each subject that we alternate to from long term memory to short term memory.  The act of retrieval helps to learn them better.

In an experiment, participants were asked to answer trivia questions. The participants were told that they would be tested later, and the nature of the test was made very clear: They would be given a blank sheet of paper and, without being asked the questions again, they would be asked to free-recall the answers. During the question answering phase, after each correct answer, the participants were asked to predict the probability that they would be able to recall the answer again on the final test. The results were surprising: The more confident participants were that they would recall an answer, the less likely they were to recall it. This outcome occurred because participants predicted they would be able to recall the answer to questions that they answered quickly, but they were most likely to free-recall answers that they had thought about for a long time.  This means that efforts to process the questions and look for the answers in memory strengthened learning and increased the likelihood of retrieving the information later.  

In another study, researchers presented to participants paintings by 12 different artists. Some artists’ paintings were presented on consecutive trials while other artists’ paintings were presented interleaved with other paintings. As a test, participants were asked which artist painted each of a set of previously un-presented paintings. They were more accurate following interleaved (i.e., spaced) learning than following blocked (i.e., massed) learning. Blocking may have made it easier to notice similarities within a given artist’s paintings, whereas the value of interleaving appears to lie, at least in part, in highlighting differences between categories.  The comparison process causes the mental representation to be more abstract, with conceptualization of the categories of the differences and the relations between them. 

In the domain of motor skills there is substantial support for the idea that interleaving practice on separate skills to be learned, such as the several strokes in tennis, requires that motor programs corresponding to those skills be repeatedly reloaded, rather than executed over and over again, which has learning benefits.

The authors recommend designing textbooks that don’t mass one topic at a time but periodically return to prior topics in an effort to promote spaced learning.

What about mistakes and errors made during learning?  Anticipating, unsuccessfully, a to-be-learned response can enhance learning.  If participants are asked to predict what associate of a given cue word (e.g., Whale) is to be learned before they are shown the actual to-be-learned target (e.g., Mammal), their later cued recall (e.g., Whale: __?__) of the target word is enhanced, versus a pure study condition (e.g., Whale: Mammal), even when the predicted associate  differs from the target associate.

Making errors is often an essential component of efficient learning.  Manipulations that eliminate errors can often eliminate learning. Thus, for example, when retrieval of to-be-learned information is made so easy as to insure success, the benefits of such retrieval as a learning event tend to be mostly or entirely eliminated.  Introducing desirable difficulties into learning procedures, for example, such as variation or interleaving, tends to result in more errors being made during the acquisition process, but it also tends to enhance long-term retention and transfer.   Making  errors appears to create opportunities for learning and, surprisingly, that seems particularly true when errors are made with high confidence.  Feedback was especially effective when it followed errors made with high confidence versus errors made with low confidence.

What predicts success in second language acquisition? Or: babies know statistics! Part B.

 Frost, R., Siegelman, N., Narkiss, A., & Afek, L. (2013). What predicts successful literacy acquisition in a second language?. Psychological science,24(7), 1243-1252.‏  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3713085/

In the previous post I wrote about the hypothesis according to which second language acquisition is like any other learning process.  In any learning process we identify and perceive systematic and probabilistic structures in our environment.  Acquiring  second ( and first) language is mainly a process of acquiring and assimilating the statistical features of our linguistic environment.

Israeli researchers Frost, Siegelman, Narkiss  and  Afek, studied this hypothesis with American students at the overseas school of the Hebrew University in Jerusalem.  The students took part in the research during their first year of learning Hebrew as a second language.

Words in Hebrew are normally composed by intertwining a tri-consonantal root, which carries the core-meaning of the word, with word-pattern morphemes in which there are “open slots” for the root’s consonants to fit into.   The typical marker of reading Hebrew is sensitivity to the location of root consonants.   The writing system of Hebrew consists of letters that mostly represent consonants (thereby root information), while most of the vowels can optionally be superimposed on the consonants as diacritical marks (“points”). These points, however, are omitted from most adult reading material. Reading Hebrew fluently cannot be carried out by simply applying grapheme-phoneme conversion rules, but requires a deep understanding of the language. 

Reading Hebrew was assessed at the beginning and at the end of the school year by three tasks. The first monitored speed of decoding of pointed nonwords and reflected the assimilation of the characteristics of the Hebrew writing system. The second monitored accuracy in naming unpointed words, reflecting the implicit learning of Hebrew phonological word patterns. The third -- cross-modal morphological priming -- directly tapped the main marker of reading in Hebrew: the assimilation of the morphological root-based composition of words. 

The authors hypothesized that there will be individual differences in students'  ability to assimilate the structural features of Hebrew, and that these differences will be linked with the students' general ability to acquire and assimilate the statistical features of their environment.

  In order to study this hypothesis, the authored  employed a visual statistical learning (VSL) task  in which 24 relatively complex visual shapes   were presented in a consecutive stream that lasted about ten minutes. The 24 shapes were organized in eight triplets ("words"), which were presented in the 10-minute stream in random order.  Participants were not told that the stream was constructed of "words". However, following this phase, participants were presented with sets of 2 shape sequences :  one was a "word" that  appeared in the stream and the other was a "non-word" made of a sequence of three shapes that were taken from the stream, but did not appear in that order. Their success rate in distinguishing the "words" from the "non-words" reflected their implicit learning of the structure of the visual shapes within the stream. 

The results showed that participants who scored well in the VSL task, that is, picked up the implicit statistical structure embedded in the continuous stream of visual shapes, on average, scored well on the tasks that monitored the assimilation of the Semitic structure of Hebrew words! This suggests that a general non-linguistic faculty of statistical learning accounts, at least to some extent, for success in second language acquisition when the first and the second languages differ in their basic statistical properties. Such an outcome also implies that a simple and short test involving visual shapes could predict the speed of assimilating a new linguistic environment, even before the first foreign word has been learnt.

What predicts success in second language acquisition? Or: babies know statistics!

Apparently, there are significant individual differences in the  people's ability to learn a second language.  What causes them?  And what predicts fast and successful acquisition of a second language?

There are two approaches that try to explain these individual differences.  Probably the best explanation combines both approaches.

The first approach argues that the acquisition of a second language is dependent on the same linguistic abilities that allowed for the first language acquisition.  This approach is supported by studies showing that phonological awareness, syntactic knowledge, orthographic knowledge and vocabulary in the first language usually predict success in second language acquisition.  When a child immigrates with high cognitive academic language proficiency in this first language (a level which is usually achieved through reading), he usually acquires high cognitive academic proficiency in the second language  faster and better than a child immigrating with only basic communication skills in his first language.  According to this view, linguistic measures are the best predictors of success in second language acquisition.

The second approach argues that second language acquisition is like any other learning process.  In any learning process we identify and perceive systematic and probabilistic structures in our environment.  Acquiring  second ( and first) language is mainly a process of acquiring and assimilating the statistical features of our linguistic environment.

What does this mean?

Statistical learning refers to the cognitive process by which repeated patterns, or regularities, are extracted from the sensory environment. Such learning often happens without an intention to learn and without an awareness of what was learned.

How do babies acquire language, and how do they identify words in the stream of voice sounds they hear when someone talks to them?  Saffran and colleagues showed that 8-month old 2 infants are sensitive to auditory statistical regularities. They exposed infants to a stream of syllables, constructed from 12 syllables (e.g., tu, pi, ro, bi, da, ku, go, la, bu, pa, do, ti) that formed four tri-syllabic “words” (e.g., tupiro, bidaku, golabu, padoti). After a 2- minute exposure to streams such as “bidakupadotibidakugolabutupiro …”, infants were tested in a habituation procedure with two tri-syllables. One was a “word” (e.g., “bidaku”) heard during the 2 min-exposure phase, and the other was a foil (e.g., “tudabu”). The foils were composed of three syllables that were never paired together. Saffran and colleagues found that infants showed more interest in the foil than in the word, as indexed by increased listening time (e (i.e., the duration of showing interest in each tri-syllable). That is, infants are capable of statistical learning after just two minutes of exposure to sound sequences.

Oral  and written words in any language are built by rules that restrict and determine their internal structure (for example, the sound sequence "shchtz" is not possible in English, and "lpstzr" cannot be an English word).  Each language has its own statistical structure, and when people acquire a second language, they implicitly acquire, according to the second approach, a new set of statistical rules.

Thus, according to the second approach, the fundamental cognitive faculty of implicit correlation-learning which underlies any form of learning plays a primary role in second language acquisition.

The degree of similarity between the statistical characteristics of the first and second languages can affect the process of statistical learning of the second language structure.  Like any other cognitive ability, individual differences in sensitivity to correlations in the environment can affect second language acquisition.

Is it really so?  And how do we measure it?  In the next post.

What's better – discovery learning or direct instruction? It depends on learner's characteristics (too).

Student Learning: What Has Instruction Got to Do With It? Hee Seung Lee and John R. Anderson  Annu. Rev. Psychol. 2013.64:445-469  http://www.usc-dr-edens.org/uploads/7/2/5/3/7253252/annurev-psych-student_learning__and_instruction.pdf

Discovery learning is a learning style in which students receive minimal instruction and construct their own knowledge.  Direct instruction is the traditional learning style of teaching the material and then practicing it.  Which style is better?

Although this question is interesting and relevant for every study domain, most research cited in this interesting paper was done in math.  I hope the conclusions can be generalized to other domains.

Learning conditions that introduce certain difficulties during instruction, as happens in discovery learning, appear to slow the rate of learning but often lead to better long-term retention and transfer than learning conditions with less difficulty.

This argument was assessed in a study in which students were trained to decipher cryptograms with different forms of instructional methods. The researchers compared students who were given explicit rules followed by problem practice with students who just tried to solve the problems and had to discover the rules. The discovery students did better on transfer problems that required new rules.

In another study,   children in discovery classrooms  used a variety of physical manipulatives and worked in pairs to solve mathematical problems. After working in pairs, a teacher led a whole-class discussion, and the children talked about their interpretations and solutions. After a while, students were given a standardized achievement test. The results showed that students in discovery and regular classrooms were not different in terms of the level of computational performance. However, the students in the discovery classrooms demonstrated higher levels of conceptual understanding than those in the regular classrooms.

In another study, children were traced for three years to assess understanding of concepts and procedures on multidigit addition and subtraction. The study compared students who used an invented strategy with students who used a standard algorithm. Students who invented a strategy were able to use not only their own invented strategy (if asked to do so), but also the standard algorithm after they learned that. Invention students also showed better understanding of base-ten number concepts and better performance in a transfer task. On the other hand, the algorithm group showed significantly more buggy algorithms in their problem solving than did the invented-strategy group,   implying that they depended on the use of learned procedures and lacked a deep conceptual understanding about the computation procedures.

 Discovery learning is believed to increase students’ positive attitudes toward learning. Learning through exploration allows students to have more control in a task, and this in turn fosters more intrinsic motivation. In addition, it is argued that discovery learning enables students to learn additional facts about the target domain.

The discovery learning approach appears to be effective only with high levels of practice and more time for the learning phase than allocated in direct instruction. In the early phases of learning, students who are learning through discovery make more errors and understand the material less.  Only when they have enough time to learn and practice they enjoy the advantages of discovery learning.

Another instruction method that works well is worked examples. Worked examples   provide an expert’s solution that students can emulate. Students  are typically given step by-step solution steps, and a final answer to the problem. Worked examples are usually alternated with problems to be solved.  Worked examples are very effective in the early phase of learning.  Students who are prompted to generate their own explanations for worked examples show  greater learning gains than those who are prompted to paraphrase provided explanations for the same example.

In another study, 7^th grade students solved multistep equations.  One student group studied sets of two differently solved solutions to the same problem.  The students were asked to compare the two solutions and to contrast them.  The two solutions were written on the same page, and each step was named.  Another student group studied the same sets of two worked examples, but each was presented on a different page.  The students were not asked to compare and contrast them but rather to think about each solution.  After two days of this intervention, the student's conceptual knowledge,  procedural knowledge  and procedural flexibility were tested.  The students who compared between the two worked examples gained more procedural knowledge and more flexibility, and had a better transfer ability than students who learned each example by itself.  There was no difference between the groups in conceptual knowledge.

Often it's best to integrate discovery learning and direct instruction. In one study, students learned the concept of density. In the direct instruction condition, student were told the relevant concepts and formulas on density and then practiced with contrasting cases. In the discovery condition, students had to invent formulas with the same contrasting cases first, and then formulas were provided only after they completed all the inventing tasks. Both groups of students showed a similar level of proficiency at applying a density formula on a word problem; however, the invention students showed better performance on the transfer tests that also required an understanding of ratio concepts but had semantically unrelated topics.   The direct instruction students did not have a chance to find the deep structure because they simply focused on what they had been told and practiced applying the learned formulas. The inventing activity appeared to serve as preparation for future learning, and thus when the expert solutions were provided later, these students could appreciate the expert solutions better than those who were not prepared. Even though most students fail to generate valid methods on their own during the invention phase, this failure experience actually helps students become prepared to learn better in the following learning phase by activating students’ prior knowledge and having students attend to critical features of the learned concepts. 

Discovery learning is not always better than direct instruction.  It depends on learner characteristics. Experienced learners (those who have prior knowledge of the material) benefit more from minimal instruction (that is, discovery learning).  Students with high levels of prior knowledge benefit more from comparing and contrasting two worked examples.  Students with low levels of prior  knowledge learn less well this way.  Comparing and contrasting only overloads them.  High ability students benefit more from discovery learning than low ability students.   When they have difficulties, high ability students tend to lean more on previously studied examples than low ability students.  High ability students spend more time studying worked examples than low ability students.  They also have more solution ideas, they can explain the solutions better, and they can identify their misunderstandings better than low ability students.  Students with low ability gain more from direct instruction.

When do we learn best? When the material is a little familiar – and a little difficult!

Ian Leslie, in his interesting book "Curiosity", writes about psychologist Daniel Berlyne who gave people geometrical shapes to look at.  The shapes differed in complexity.  He discovered that people got bored quickly when they looked at simple shapes.  They were more interested to look at complex shapes.  But when the shapes were extremely complex, they lost interest again.

We tend to be less interested in subjects about which we feel we know everything, as well as in subjects about which we feel we don't know anything.  We tend to be more interested in things we know something about. The partial information we have fuels our curiosity by creating in us an awareness of our ignorance and a desire to know more.  

  

This is why children who seem uncurious sometimes just lack basic knowledge about the subject.  Supplying them with this knowledge may ignite their curiosity.

When we make a little effort, we learn better.  People solved puzzled better when they were distracted by a series of digits read out loud throughout the task, than without this distraction.  When people were requested to spell some of the letters in word pairs they learned, they recalled the pairs better than when required to only memorize the pairs without spelling.  Making learning more active and effortful by spelling improved the ability to remember the information. 

A subjective feeling that the material is a bit difficult makes people process it better, deeper, more attentively and to understand it better.  

Apparently, even changing the font can cause such an effect.  In a study (the details of which are presented below), one group of university students learned a text that was presented in a readable font.  Another student group learned the same text in a less readable font, printed in gray.  The students were then asked questions about the text's content. Students who learned the text printed in the less comfortable to read font did significantly better on the questions than students who studied the readable text!

In another study, with 222 high school students, the researchers selected pairs of classes which were taught by the same teacher in the same level.  The subjects taught were English, Physics, History and Chemistry.  In one class of each pair, the researchers changed the font of the materials which were handed out by the teachers to a less readable font.  In the other class of each pair the materials were untouched.  Students who learned materials printed in less readable font got significantly better grades than students who learned the original materials!

The researchers argue that the change in font made the material itself feel more difficult.  This caused the students to work harder and to learn it better.

Diemand-Yauman, C., et al. Fortune favors the bold and the italicized : Effects of disfluency on educational outcomes. Cognition (2010). http://198.65.234.49/Aktuell/11/01_14/cognition.pdf