THE BIG BOOK OF COMPUTING PEOAGOGY

Page 104

RESEARCH

ADDRESSING STUDENTS’ ALTERNATIVE CONCEPTIONS Addressing misconceptions in computing develops students’ understanding, development and confidence lternative conceptions (often referred to as misconceptions) are beliefs commonly held about a concept by students that are overly simplified or inaccurate. Where these beliefs contradict with reality or accepted scientific understanding, they can cause confusion and affect students’ efficacy and, ultimately, their performance.1 It is therefore vital that computing educators understand commonly held alternative conceptions, how they arise, and how they can be addressed.

A

Although there is little research that has evidenced this, we suspect there are a number of alternative conceptions around computing. We must therefore also look to research in other subjects that form the traditions that underpin computing, in particular maths, science, and engineering. Some psychologists claim that alternative conceptions can be very persistent.2 In presenting learners with accurate conceptions that challenge their existing understanding, a state of “cognitive disequilibrium”3 is reached, in which

Sources of alternative conceptions

SUMMARY Alternative conceptions (commonly known as

■ Factual misconceptions derive from false facts

misconceptions) can develop when new knowledge

or information that have been assimilated into

conflicts with a learner’s existing mental models.

memory without being challenged

Alternative conceptions can be categorised as:

Become familiar with commonly occurring misconceptions:

■ Preconceived notions are intuitive but

inaccurate leaps made about new ideas, based on existing knowledge ■ Non-scientific beliefs can arise when a

learner’s mental models have been informed

■ Review existing research into alternative

conceptions in computing ■ Reflect on your own experience ■ Share common alternative conceptions with

your peers and the community

by non-authoritative sources and are counter to accepted science ■ Conceptual misunderstandings occur when

instruction fails to challenge existing mental

Identify alternative conceptions through: ■ Varied opportunities for classroom talk ■ Diagnostic multiple-choice questions

models and learners attempt to resolve these independently with mixed success ■ Vernacular misconceptions occur where

104

Effective ways to address alternative conceptions include:

new terminology is the same as that used in

■ Constructing individual or group concept maps

another context or everyday language, but with

■ Reach consensus around a concept using

another meaning

The Big Book of Computing Pedagogy

learners must reconcile the conflicting pieces of information. While this creates an opportunity to replace an alternative conception, learners may choose to discard accurate information that doesn’t fit with their existing mental models. Educators therefore need to be aware of common alternative conceptions that their learners may hold. They should develop a range of strategies that support learners through their misconceptions, encouraging them to recognise them as wrong, but without labelling them in this way.

peer instruction

According to Piaget, learners build new understanding by combining experience with existing mental models. Educators help facilitate this learning by providing learning experiences and supporting learners to integrate the experience with their existing understanding. An alternative conception can arise in a number of ways when learners’ experiences and their mental models interact in different ways. Research4 from science education proposes five categories of alternative conceptions: ■ Preconceived notions involve learners making intuitive conceptual leaps based on their everyday experience. They do not have sufficient relevant experience of a concept or phenomenon, so they use their existing experience to fill in the gaps. For example, learners who are used to Scratch, which automatically handles concurrent execution of code, may expect similar behaviour from Python.


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ART AND ALGORITHMS

5min
pages 154-155

THE INCLUSIVE CLASSROOM

6min
pages 152-153

PHYSICAL COMPUTING

5min
pages 130-131

REFLECTIONS

9min
pages 134-136

A PATH TO AGENCY

4min
pages 122-123

STORYTELLING

3min
pages 146-147

RETRIEVAL PRACTICE

10min
pages 148-151

VARIETY IN TEACHING

7min
pages 143-145

PHYSICAL COMPUTING IN THE CLASSROOM

5min
pages 132-133

DIGITAL PROJECTS

7min
pages 118-121

ASSESSMENT FOR LEARNING

4min
pages 106-107

MULTIPLE CHOICE

3min
page 111

METAPHORS AND MISCONCEPTIONS

8min
pages 108-110

PROJECT-BASED LEARNING

5min
pages 116-117

WATCH AND LEARN

5min
pages 98-99

ALTERNATIVE CONCEPTIONS

6min
pages 104-105

MODELLING FOR LEARNERS

6min
pages 96-97

VIDEOS AND SELF-EXPLANATION

3min
pages 94-95

LIVE CODING

6min
pages 92-93

WORKED EXAMPLES

6min
pages 90-91

WRITING CODE

5min
pages 82-83

PARSON’S PROBLEMS

6min
pages 80-81

READ BEFORE YOU WRITE

5min
pages 70-71

CODE TRACING

5min
pages 68-69

THE BLOCK MODEL

6min
pages 78-79

ENCOURAGING TALK

5min
pages 62-63

COLLABORATIVE PROBLEM-SOLVING

4min
pages 60-61

PEER INSTRUCTION

6min
pages 56-57

PAIR PROGRAMMING

6min
pages 58-59

GO UNPLUGGED

2min
page 49

ENGINEERING SKILLS

3min
page 41

SCRATCH ENCORE

3min
page 40

SEMANTIC WAVES

7min
pages 46-48

SCRATCHMATHS

4min
pages 38-39

LEARNING THROUGH MAKING

5min
pages 36-37

CULTURALLY RELEVANT PEDAGOGY

6min
pages 34-35

THE ‘RIGHT’ WAY?

6min
pages 14-15

THE PRIMM APPROACH

7min
pages 22-24

CODING & 21ST-CENTURY SKILLS

4min
pages 28-29

COGNITIVE LOAD THEORY

5min
pages 20-21

CONCEPT MAPS

6min
pages 10-12

CURRICULUM DESIGN

8min
pages 30-33

UDL

6min
pages 25-27

VELA CONCEPTS

2min
page 13
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