teaching methods are erroneous: approaches which lead to ...in international handbook of research on...

Teaching methods are erroneous:

approaches which lead to erroneous

end-user computing

Mária Csernoch, Piroska Biró

[email protected]

Faculty of Informatics, University of Debrecen, Hungary

Pólya (1954)

How To Solve It

• understand the problem, see clearly what is

required

• see how the various items are connected, how

the unknown is connected to the data, in order to

obtain the idea of the solution to make a plan

• carry out the plan

• we look back, at the completed solution, we

review and discuss it

End-user problem solving

• understand the problem, see clearly what is

required

• see how the various items are connected, how

the unknown is connected to the data, in order to

obtain the idea of the solution to make a plan

• carry out the plan

• we look back, at the completed solution, we

review and discuss it

Bricolage!

Computer problem solving approaches

Mathability of software tools

computer algorithmic and

debugging based (CAAD)

trial-and-error wizard

based (TAEW)

deep approaches

surface approaches

algorithmic based

information based

concept based

Csernoch & Biro (2015) Computer Problem Solving. In Hungarian: Számítógépes problémamegoldás, TMT, Tudományos és Műszaki Tájékoztatás, Könyvtár- és információtudományi szakfolyóirat, vol. 62, no. 3, 2015, pp. 86–94.

5

4

32

1

Belief in a “Fixed”

nature of science

(e.g. simple absolute

truths)

Performance Goals:

Students should

demonstrate knowing

truths on a test

High Teaching

Self-Efficacy

Low Teaching

Self-Efficacy

Teaching by preparing students

to do well on recalling the

science canon and performing

well on tests.

Teacher incapable of preparing

students for performing well on

tests.

Teaching by providing students

opportunities to understand and

appreciate science deeply. Likely

will use methods to reveal the

dynamic nature of science.

Teacher does not provide

students with opportunities to

develop deep understanding and

appreciation of science. Likely

reverts to preparing students for

doing well on tests.

Belief in an

“Incremental” nature

of science (e.g.

dynamic contextual

knowledge)

Mastery Goals:

Students should develop

deep understanding and

appreciation for science

High Teaching

Self-Efficacy

Low Teaching

Self-Efficacy

Meaning System Model (Chen et al. 2015)

Chen, J. A., Morris, D. B. and Mansour, N. (2015), Science Teachers’ Beliefs. Perceptions of

Efficacy and the Nature of Scientific Knowledge and Knowing. In International Handbook of

Research on Teachers’ Beliefs. (Eds.) Fives, H. & Gill, M. G. Routledge, pp. 370–386.

FixedPerformance

Goals

Teaching by preparing students to

do well on recalling the science

canon and performing well on

tests.

Teaching by providing students

opportunities to understand and

appreciate science deeply. Likely will use

methods to reveal the dynamic nature of

science.

Teacher does not provide students with

opportunities to develop deep

understanding and appreciation of

science. Likely reverts to preparing

students for doing well on tests.

Incremental Mastery Goals

High Teaching

Self-Efficacy

Low Teaching

Self-Efficacy

High Teaching

Self-Efficacy

Low Teaching

Self-Efficacy

Belief in the

nature of science

e.g. simple absolute

truths

e.g. dynamic

contextual knowledge

Goals

Students should

demonstrate knowing truths

on a test

Students should develop deep

understanding and

appreciation for science

Teacher incapable of preparing

students for performing well on tests.

Result

Meaning System Model

(Chen et al. 2015)

Problems

• spreadsheet errors are due to Attention Mode

(ATM) thinking (Panko, 2015)

• end-user computing invisible to the central

corporate IT group, to general corporate

management, and to information systems

researchers (Panko & Port, 2013)

• the general public identify computer science with

a computer driving license (Hromkovic, 2009)

Teaching – Problems

• low mathability approaches to end-user computing

• teaching materials do not support the development of computational thinking

• teachers, almost unconditionally, accept these teaching materials

• profit-oriented software companies’ “user-friendly” slogans

• teacher education is not prepared for the challenges of the digital era

Misconceptions

• “…children bored out of their minds being taught

how to use Word and Excel by bored

teachers…” (Gove, 2012)

• “…a collection of low-level routine knowledge

such as how to format pages in a word

processor, or how to make tables in HTML.”

(Bell & Newton, 2013)

“User-friendly” software

???????

• software companies– user-friendly

• increasing number of functions, methods

• novel surfaces, features

– help tools• constant-based

• focusing on the language

– do not develop computational thinking

– information and TAEW-based methods

• curricula, teachers

• computer paradox


based (TAEW)

surface approaches

information based

2

1

TEACHING MATERIALS

Spreadsheet?

Teaching menu bar?

It is boring. It is incorrect. It is out of space.

Textbook examples

• fabricated examples

• tables, data

– non-existing

– meaningless

• list(s) of functions

It is boring.

It is useless. It is meaningless.

41 functions!

Without table!

It is incorrect.

Exceptions

It is uninformative.

if the items of the vector are match type

in ascending order 1

in descending order −1

not ordered 0

It is misleading.

Number of functions

• “One can learn programming by starting with five

instructions only and working totally with about

fifteen instructions…” (Hromkovic, 2008)

• “People in average do not use more than a

dozen functions.” (Walkenbach, 2010)

• limited capacity of working memory – “magical

number seven” (Miller, 1956; Kahneman, 2011)

• “List the 15 spreadsheet functions which you

think are the most important.” (Csernoch et al.,

2014)

Lists of functions

SUM

AVERAGE

MIN

MAX

IF

INDEX

MATCH

VLOOKUP

HLOOKUP

COUNT

COUNTA

COUNTIF

SUMIF

AVERAGEIF

COUNTIFS

SUMIFS

ROUND1

SMALL

LARGE

LEFT

RIGHT

LEN

SEARCH

ISERROR

COUNTBLANK

VALUE

MIDDLE

STDEV

OR

AND

DGET

DSUM

SUMPRODUCT

CONCATENATE

HOUR

PERC

ROUND

DATE

TEXT

FLOOR

ROUNDUP

ROUNDDOWN

POWER

SQRT

TODAY

LOOKUP

CHAR

REPLACE

DCOUNT

FREQUENCY

YEAR

MONTH

DAY

TIME

PRODUCT

RAND

ABS

FACT

DMIN

DAVERAGE

DMAX

MEDIAN

DEGREES

ISNUMBER

LOG

MODUS

PMT

SIN

RANK

INT

DAYS360

COLUMN

ROW

SUBSTITUTE

MODUS

VAR

IFERROR

NOW

COS

ISNA

WEEKDAY

CELL

RATE

SECOND

NOT

QUARTILE

TRIM

DATEVALUE

ARCSIN

ARCCOS

DGET

TRUNC

TAN

DSTDEV

DCOUNTA

DPRODUCT

CHOOSE

HYPERLINK

CRITBINOM

PI

LOOKUP

IRR

EXACT

AVEDEV

DEC2BIN

FV

IGAZ

HAMIS

REPT

LOWER

UPPER

PROPER

COMBIN

CODE

OFFSET

TIMEVALUE

INFO

DECIMAL

SIGN

LINEST

PV

DDB

EXP

ROMAN

CORREL

NORMINV

LN

LOG10

PERMUT

RADIANS

SUBTOTAL

SUMSQ

GEOMEAN

HARMEAN

FORECAST

NPER

AVERAGEA

NETWORKDAYS

FIND

ISTEXT

TREND

NPV

ISPMT

ROWS

DAYSINMONTH

DAYS

WEEKSINYEAR

ACCRINT

AMORDEGRC

ARCTAN

TYPE

AVERAGEIFS

DURATION

EASTERSUNDAY

VDB

SLN

DB

PPMT

IPMT

DSTDEVP

ERROR.TYPE

CEILING

EVEN

ODD

TRANSPOSE

USDOLLAR

NA

EFFECT

ISBLANK

SUMPOZITIVE

PURECOUNT

A Problem-Solving Approach

Gross, D., Akaiwa, F, and Nordquist, K. (2014) Succeeding in Business with Microsoft Excel 2013: A Problem-Solving Approach, Cengage Learning, US.

This book focuses on teaching how to solve

problems, although the concepts and tasks

presented could apply to a variety of computer

applications and programming languages.

The problems to be solved … are presented

within the context of a fictional … company ….

Computer cooking

Computer cooking – ECDL

Functional Modeling

Hubwieser, P., Spohrer, M., Steiner, M. and Voß, S. (2007) Informatik. Tabellenkalkulationssysteme. Datenbanken. Klett.

Sprego

Spre goSpregoadsheet Le

Sprego functions

Sprego 1 Sprego 2 Sprego 3

SUM()

AVERAGE()

MIN()

MAX()

LEFT()

RIGHT()

LEN()

SEARCH()

IF()

Sprego functions


SUM() INDEX()

AVERAGE() MATCH()

MIN() ISERROR()

MAX()

LEFT()

RIGHT()

LEN()

SEARCH()

IF()

Sprego functions


SUM() INDEX() SMALL()

AVERAGE() MATCH() LARGE()

MIN() ISERROR() ROW()

MAX() COLUMN()

LEFT() AND()

RIGHT() OR()

LEN() NOT()

SEARCH() SUBSTITUTE()

IF() OFFSET()

TRANSPOSE()

ROUND()

RAND()

INT()

Sprego contents

Sprego is unplugged

Sprego features

Math toolMath toolMath toolMath tool

Sprego functionsSprego functionsSprego functionsSprego functions

concept of functionconcept of functionconcept of functionconcept of function

nnnn----aryaryaryary functionsfunctionsfunctionsfunctions

composite composite composite composite functionsfunctionsfunctionsfunctions

Spreadsheet, math, Spreadsheet, math, Spreadsheet, math, Spreadsheet, math,

programming toolprogramming toolprogramming toolprogramming tool

array formulasarray formulasarray formulasarray formulas

nnnn----dimensional vectordimensional vectordimensional vectordimensional vector

introduction to loopsintroduction to loopsintroduction to loopsintroduction to loops

Content toolContent toolContent toolContent tool

authentic tablesauthentic tablesauthentic tablesauthentic tables

real world real world real world real world

problemsproblemsproblemsproblems

ApproachApproachApproachApproach

conceptconceptconceptconcept----basedbasedbasedbased

high high high high mathabilitymathabilitymathabilitymathability

schemata schemata schemata schemata

constructionconstructionconstructionconstruction

Programming toolProgramming toolProgramming toolProgramming tool

programmingprogrammingprogrammingprogramming

introductory introductory introductory introductory

languagelanguagelanguagelanguage

Spreadsheet toolSpreadsheet toolSpreadsheet toolSpreadsheet tool

Sprego Sprego Sprego Sprego functionsfunctionsfunctionsfunctions

introduction to introduction to introduction to introduction to

spreadsheetsspreadsheetsspreadsheetsspreadsheets

version independentversion independentversion independentversion independent

SPREGO: from ATM to AUM thinking

• Thinking modes– Attention Mode (ATM) thinking (System 2)

• erroneous !strategic problem solving!

– Automatic Mode (AUM) thinking (System 1)• expertise → information stored in memory → recognition

• Mathability 4 → 3– schemata construction (Skemp, 1971)

– recognition-primed decision (Klein, in Kahneman, 2011)

– cognitive load theory (Chi et al., 1982; Sweller et al. 1998)

– “task sets” (Kahneman, 2011)

– metaschemata (CLT)

• Teachers– ‘”What ‘some’ teachers do matters.” (Hattie, 2012)

Kahneman, D. (2011), Thinking, Fast and Slow. New York: Farrar, Straus; Giroux.


Cognitive ease

• repetition induces cognitive ease, System 1, AUM thinking (Kahneman, 2011)– Sprego: time for repetition instead of listing features

• mood effects the operation of System 1 (Kahneman, 2011), motivation– Sprego: motivating (methods, content)

• schema (Skemp, 1971)– assimilation (for new data)

– accommodation (for structural change)

– Sprego: schema construction, assimilation, accommodation


Intuition (Simon, H. in Kahneman, 2011)

• Definition

– the situation provides a cue

– this cue gives the expert access to information stored

in memory

– the information provides the answer

• Professionals to develop intuitive expertise

– the quality and speed of feedback

– sufficient opportunities to practice


computer algorithmic and

debugging based (CAAD)


based (TAEW)

deep approaches

surface approaches

algorithmic based

information based

concept based

5

4

32

1

Skemp, R. (1971), The Psychology of Learning Mathematics. Lawrence Erlbaum Associatives, New Jersey, USA.

Skemp:

intelligent learning Skemp:

habit learning


True experts – Pseudo experts

• true experts

– know the limits of their knowledge. (Klein in

Kahneman, 2011)

• pseudo experts

– do not know what they are doing (the illusion of

validity)

– Unskilled and Unaware of It (Kruger & Dunning, 1999)

– ignorant of ignorance

Back to Pólya (1954)

How To Solve It

• “If you cannot solve a problem, then there is an

easier problem you can solve it.”

• strategic procedures, implemented in System 2

(ATM thinking)

Teaching methods are erroneous:

approaches which lead to erroneous

end-user computing

Mária Csernoch, Piroska Biró

[email protected]

Faculty of Informatics, University of Debrecen, Hungary

Thank you for your attention!

teaching methods are erroneous: approaches which lead to ...in international handbook of research on...

Documents