A Guide to University Learning

Learning From Textbooks:

Student Guide
Active Reading
Reading Strategies (SQ4R)
Highlighting Pros & Cons
Note Taking Pros & Cons
Handling Difficult Texts
Textbook Readings Top Ten Takeaways
Practice Activity
Instructions
Appendix A: Textbook Reading
Appendix B: Highlighting Sample
Appendix C: Notes Sample

Student Guide

Reading and remembering information from textbooks can be one of the most challenging aspects of learning at university. The amount of reading from one course to another can vary widely, as can your professors’ expectations for how and what you read. Depending on the course, your professor might expect you to:

No matter how your professor expects you to use your textbook, there are many strategies to enhance your learning when you read university textbooks.

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Active Reading

‘Passive’ reading is what students do while they are watching TV with their textbooks open. ‘Active’ reading is a more effective reading strategy that requires:

Reading Speed + Comprehension

For textbooks that you are required to know thoroughly, try:

Reading + Concentration

To minimize distractions and increase your concentration consider:

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Reading Strategies (SQ4R)

When you are reading and trying to determine what to highlight or to include in your textbook notes, pay special attention to:

SQ4R

Most university students find that their readings are often complex and packed with information and they use many methods to cope with the reading load. One popular method, SQ4R, is a series of strategies to help you read more actively and to improve your understanding and retention of the material. Try all the strategies at first, and then choose and apply those you find most effective.

Survey

Question

Read

Respond

Record

Review

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Highlighting Pros & Cons

Is it better to highlight your textbook or take notes on a separate sheet of paper? We’ve summarized the pros and cons of each method in this section and the next.

Highlighting Pros

Highlighting Cons

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Note Taking Pros & Cons

Note Taking Pros

Note Taking Cons

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Handling Difficult Texts

At some point in your university career, you may encounter a textbook which you find difficult to understand or follow. Below are some tips to help you.


Improve your Knowledge of the Subject's Terminology

Assess your Knowledge of the Basics

It is possible that your text and even the course itself could be 'above your head' if you lack an understanding of some basic concepts in the discipline. If you are struggling with an introductory course:

Read Out Loud

Reading out loud and hearing the words can help to increase your comprehension of difficult material. If you read aloud with a classmate and take turns analyzing, explaining, and summarizing the text, you may also find that another person's perspective helps to clarify meaning.

Try Another Text

The problem may simply be that the text is poorly written, or the author's style is difficult for you. Don’t abandon your required text, but it may be helpful to find another book on the same topic in the Library. Sometimes a different explanation of the same topic is all it takes to make an incomprehensible subject more accessible.

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Textbook Readings Top Ten Takeaways

  1. Reading and remembering information from textbooks can be challenging and requires your time and attention for optimal learning
  2. Do your textbook readings before the class
  3. Interact with textbook readings (i.e., ask yourself questions, make notes) to help you remember the material better
  4. Try to do your textbook reading in a spot with few distractions and at a time when you feel wide awake and alert
  5. Read for short periods of time with breaks
  6. Try the SQ4R Method (Survey, Question, Read, Respond, Record & Review) to help improve your comprehension of textbook material
  7. While highlighting may take less time than note taking, most students highlight too much. Aim to mark up only 10-15% of the content
  8. Unlike highlighting, note taking allows you to integrate your textbook notes with your lecture notes
  9. You'll understand and remember textbook content better if you write notes in your own words
  10. For difficult texts, use a specialized dictionary, check out an introductory book on topic, read out loud, or try another text
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Practice Activity

This activity will give you the opportunity to interact with a textbook reading. Ideally, the activity will get you thinking about how you can pick out the key pieces of information and process the content effectively. The subject of this reading is "Statistics for Psychology," from the fictitious course textbook. It represents one of the readings from Week 2 of the course outline.

Instructions

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Appendix A: Textbook Reading

Statistics for Psychology

Much of psychological research involves measuring observations of particular characteristics of either a population, or a sample taken from a population. These measurements yield a set of values or scores, and this set represents the findings of the research, or data. Often, it is impractical to completely measure the characteristics of a given population, known as parameters, directly. Thus, psychologists often focus on the characteristics of samples taken from a population. These characteristics are called statistics. The psychologist then uses these sample statistics to make inferences about population parameters.

In this section we will focus on a type of statistics known as descriptive statistics. We will begin with an examination of three methods of describing a set of data using scores that seem to be typical of those found in the set. We will then look at three methods of describing how scores within the set vary from these typical scores.

Descriptive Statistics

Descriptive statistics is the name given to procedures used to collect, classify, summarize, and present data. The methods used by psychologists to collect, classify, and present data are beyond the scope of this discussion. For the remainder of this section, we will be focusing on statistical methods used to summarize psychological research data.

Measures of Central Tendency

Often, data tends to group itself around some central value. This value may, in turn, be used to describe or represent the data set as a whole. Methods of determining these central values are called measures of central tendency. There are three main measures of central tendency used by psychologists. They are the mean, the median, and the mode.

Mean

When people talk about averages, they’re often referring to the mean, which is the arithmetic average of a set of scores. You have probably calculated the mean of a set of psychology scores many times in the past. Every time you sum a set of scores and divide that sum by the total number of scores you have calculated the arithmetic mean of those scores.

As you probably know from experience, the mean can be affected by extreme scores. For example, if a student were to receive five test marks over 90% and one test mark less than 20%, (let us say marks of 98%, 96%, 94%, 94%, 92%, and 18%), the mean of the test scores would be (98 + 96 + 94 + 94 + 92 + 18) / 6 = 82. Obviously, the mean in this case has been pulled in the direction of the score of under 20%. For this reason, the mean can be very misleading description of a set of scores with a heavily skewed distribution.

Median

The median is the middle score in a set of scores that have been ranked in numerical order. In cases where there are an even number of scores, the median lies between the two middle scores, and is given the value of the midpoint between those scores. Of course, if the middle two scores in an even number of scores are the same, the median has the same value as the two scores themselves. There is no formula for quickly calculating the median without doing some initial data analysis. Typically, when dealing with large data sets, researchers construct a frequency distribution representing all the scores in the data set. This allows them to use a formula to calculate each measure of central tendency using the information provided by the frequency distribution.

Unlike the mean, the median is a good measure of central tendency to use when describing a heavily skewed set of scores. Returning to our example from above, our student’s median test score would be 94%, which is a much better indication of the student’s overall performance. Thus, the median is a better representation of the scores within a skewed data set than is the mean. In fact, the median is the best method of central tendency to use when describing skewed data.

Mode

The mode is simply the most frequently occurring score in a data set. Returning once again to the test scores of our sample student, the mode for this data set is would be 94%, as it occurs twice within the data set. If two scores occur equally often within a data set, the set has two modes and is termed bimodal. Any data set that has two or more modes can be referred to as multimodal.

Like the median, there is no formula for calculating the mode without conducting at least some preliminary data analysis. For small data sets the mode may simply be determined by comparing the number of times the most popular scores appear in the set.

Measures of Variability

Almost all data sets demonstrate some degree of variability. In other words, data sets usually contain scores which differ from one another. Only under very rare circumstances to researchers encounter data sets which have no variability. Needless to say, of the few sets of data which demonstrate no variability, fewer still will be of any interest to psychological researchers. The truly interesting observations are those of characteristics which vary within a population or sample.

This variability cannot be captured or shown by measures of central tendency. For example, if two data sets have the same mean, there is no guarantee that the two sets are very similar at all. What is needed are measures of variability which allow the researcher to determine the degree of variation within a population or sample, and thus to determine just how representative a particular score is of the data set as a whole. This in turn allows the researcher to determine the scope and validity of any generalizations
he or she wishes to make based on his or her observations. The measures of variability used by researchers include the range, the variance, and the standard deviation.

Range

The range is simply the difference between the highest and lowest scores in a distribution, and is found by subtracting the lowest score from the highest score. This measure of variability gives the researcher only a limited amount of information, as data sets which are skewed towards a low score can have the same range as data sets which are skewed towards a high score, or those which cluster around some central score. The range is, however, useful as a rough guide to the variability demonstrated by a data set, as it tells the researcher how a particular score compares to the highest and lowest scores within a data set. For example, a student might find it useful to know whether his or her score was near the best or worst on an exam.

Variance

A more informative measure of variability is the variance, which represents the degree to which scores tend to vary from their mean. This tends to be more informative because, unlike the range, the variance takes into account every score in the data set. Technically speaking, the variance is the average of the squared deviations from the mean.

To calculate the variance for a set of quiz scores:

Standard Deviation

More informative still is the standard deviation, which is simply the square root of the variance. You may be asking yourself ‘Why not simply use the variance?’ One reason is that, unlike the variance,
the standard deviation is in the same units as the raw scores themselves. This is what makes the standard deviation more meaningful. For example, it would make more sense to discuss the variability of a set of IQ scores in IQ points than in squared IQ points.

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Appendix B: Highlighting Sample

  • Visual representation of the highlighting sample
  • A text version of what was highlighted is found below
  • Introduction:

    Descriptive Statistics:

    Central Tendency:

    Mean:

    Median:

    Mode

    Variability

    Range

    Variance

    Standard Deviation

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    Appendix C: Notes Sample

  • Visual representation of the textbook notes
  • Text version of the content included in the textbook notes is found below
  • Statistics for Psychology

    Research Characteristics of Population

    Descriptive Statistics

    Measures of Central Tendency

    Mean

    Median

    Mode

    Measures of Variability

    Range

    Variance

    Standard Deviation

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