Academic Text Structures

ACADEMIC TEXT STRUCTURE: CAUSE-EFFECT

  • Explains why something happens.
  • Word Signals:
    • Expressions indicating effect, consequence, or result: so, so that, as a result, consequently, explanation for, thus, accordingly.
    • Expressions indicating cause: due to, as a result of, a cause of.

Visual Representation:

  • Cause → Effect 1
  • Cause → Effect 2
  • Cause → Effect 3
Sample Text:
  • Example: Humans unintentionally changing an ecosystem.
  • Cause: New homes built in a wooded area.
  • Effects:.
    • Deer and wild animals lost their habitat.
    • Deer roam into the city (unusual to see them on main street).

ACADEMIC TEXT STRUCTURE: PROBLEM-SOLUTION

  • Writer presents a problem that needs to be solved.
  • Word Signals:
    • The problem/dilemma is…
    • if/then, so that, to solve.
    • an answer to, to address(es) the problem.

Visual Representation:

  • Problem → Solution 1
  • Problem → Solution 2
  • Problem → Solution 3
Sample Text:
  • Problem: Surge in teen pregnancies.
  • Dilemma: Teen pregnancies make it difficult for young mothers to pursue dreams and meet infant demands.
  • Solutions:
    • Birth control (not 100% effective).
    • Abstinence (seen as an effective way to solve the problem).

ACADEMIC TEXT STRUCTURE: COMPARISON and CONTRAST

  • Comparison explains similarities.
  • Contrast explains differences.
  • Word Signals:
    • Similarities: Similarly, in like manner, in the same way.
    • Differences: On the other hand, on the contrary, the opposite, compared to, in contrast, although, unless, however.
    • Comparative and superlative words: Better than, more; best, most.

Visual Representation:

  • Similarities
  • Differences
Sample Text:
  • Acoustic & Electric Guitars: similarities and differences.
  • Similarities:
    • Both use a body for neck attachment.
    • Both use a neck with frets for finger placement.
    • Strings attach to the body's lower end and go to the head.
    • Both use strings that vary in gauge.
    • Each is tuned to produce the proper tone desired
  • Difference:
    • Varied sounds

ACADEMIC TEXT STRUCTURE: ENUMERATION

  • A rhetorical device used for listing details or steps.
  • Word Signals: first, second, in addition, next, then, another, finally, and so.
  • Enumerating through numbering: 1., 2., 3., 4.
  • Using bullets: … … … …
Sample Text:
  • Immigrating to Finland
    • First, everyone speaks English.
    • Second, Finland has a superior, free health system.
    • In addition, most public transport is free.
    • A third reason is that Finns are friendly, outgoing people.
    • Finns also value equality between the sexes.
    • Finally, where else except in Finland can you swim outside during the winter.

ACADEMIC TEXT STRUCTURE: CLASSIFICATION

  • Distinguishes types or classes.
  • Explains seemingly unrelated information.
  • Language Cues:
    • There are several types/kinds of.
    • A part of.
    • An example of.
    • Groups/kinds/ways/types/classes of.
    • Another kind of.
    • Divided into.

Visual Representation:

  • Main Concept
    • Sub-Concepts
    • Semi- Concrete
    • Abstract
    • Concrete
    • Generalization
    • Examples
Sample Text:
  • Stringed instruments:
    • Violin family: violin, viola, cello, double bass.
    • Fretted instruments: banjo, mandolin, lute, ukulele, guitar.

ACADEMIC TEXT STRUCTURE: THESIS-EVIDENCE

  • Thesis: Provides the controlling idea; should be original, assertive, and arguable.
  • Evidence: Support or logical proof.
  • Word Signals: assert, claim, prove/show that…, support and alleged; nouns like evidence, proof, and argument.

Visual Representation:

  • Thesis → Evidence 1
  • Thesis → Evidence 2
  • Thesis → Evidence 3
Sample Text:
  • Thesis: Communicate nonverbally is effective.
  • Evidence:
    • Parents' nonverbal cues (noisy dish placement, stomping).
    • Teacher's nods and smiles.
    • Rose given by a boy makes a loud declaration.

Topic Summary

Identifying the Academic Text Structures as well as their Word Signals and Visual Representation helps READERS to understand the Nature, Purpose and Organization of the Texts.

Evaluation Activity

Identify the common structures of an academic text by picking out the signal words from the list and classify them accordingly.

Sample Text 1

  • I do well in school, and people think I am smart because of it. But it’s not true. In fact, three years ago I struggled in school. However, two years ago I decided to get serious about school and made a few changes. First, I decided I would become interested in whatever was being taught, regardless of what other people thought. I also decided I would work hard every day and never give up on any assignment. I decided to never, never fall behind. Finally, I decided to make school a priority over friends and fun. After implementing these changes, I became an active participant in classroom discussions. Then my test scores began to rise. I still remember the first time that someone made fun of me because “I was smart.” How exciting! It seems to me that being smart is simply a matter of working hard and being interested. After all, learning a new video game is hard work even when you are interested. Unfortunately, learning a new video game doesn’t help you get into college or get a good job.

Sample Text 2

  • Rule-based approaches classify text into organized groups by using a set of handcrafted linguistic rules. These rules instruct the system to use semantically relevant elements of a text to identify relevant categories based on its content. Each rule consists of an antecedent or pattern and a predicted category. Say that you want to classify news articles into two groups: Sports and Politics. First, you’ll need to define two lists of words that characterize each group (e.g., words related to sports such as football, basketball, LeBron James, etc., and words related to politics, such as Donald Trump, Hillary Clinton, Putin, etc.). Next, when you want to classify a new incoming text, you’ll need to count the number of sport-related words that appear in the text and do the same for politics-related words. If the number of sports-related word appearances is greater than the politics-related word count, then the text is classified as Sports and vice versa. For example, this rule-based system will classify the headline “When is LeBron James' first game with the Lakers?” as Sports because it counted one sports-related term (LeBron James) and it didn’t count any politics-related terms. Rule-based systems are human comprehensible and can be improved over time. But this approach has some disadvantages. For starters, these systems require deep knowledge of the domain. They are also time-consuming, since generating rules for a complex system can be quite challenging and usually requires a lot of analysis and testing. Rule-based systems are also difficult to maintain and don’t scale well given that adding new rules can affect the results of the pre-existing rules.