Computational Thinking is Informational Thinking Greg Michaelson School

Computational Thinking is Informational Thinking Greg Michaelson School of Mathematical & Computer Sciences Heriot-Watt University G. Michaelson@hw. ac. uk http: //www. macs. hw. ac. uk/~greg 21/11/2012 Computational Thinking is Informational Thinking 1

Models • we make models of the world to: – understand it – change it in predictable ways Columbus Globes http: //www. maps 2 anywhere. com/Globes/columbus_world_globes_01. htm 21/11/2012 Computational Thinking is Informational Thinking 2

Programs and models world • a program: – maps the abstractions of a conceptual model 21/11/2012 Computational Thinking is Informational Thinking brain model 3

Programs and models world • a program: – maps the abstractions of a conceptual model – onto the concrete behaviours of a physical computer brain model program computer 21/11/2012 Computational Thinking is Informational Thinking 4

Programs and models world • a program: – maps the abstractions of a conceptual model – onto the concrete behaviours of a physical computer – so the behaviour of the program on the computer tells us about the world brain model program prediction • computing computer 21/11/2012 Computational Thinking is Informational Thinking 5

How do we. . . ? • make models? – solve problems • realise models as programs? – write programs – programming Then a miracle occurs. . . http: //www. sciencecartoonsplus. com/pages/gallery. php 21/11/2012 Computational Thinking is Informational Thinking 6

Discourse & method • should we separate out: – problem solving – model making – programming? • are they inextricably linked? 21/11/2012 Computational Thinking is Informational Thinking 7

Discourse & method • we need languages/notations to: – solve problems – make models – write programs • should these be the same or different? 21/11/2012 Computational Thinking is Informational Thinking 8

Computing Science? • academic discipline • underpins all ICT – model making – tool building • theory & practice of computation – making models of reality from information structures & algorithms – animating models on computers • programming bridges models and computers 21/11/2012 Computational Thinking is Informational Thinking 9

Computing Science? • is programming the “new Latin”? • should we teach all students to program? Observer 1/4/12 21/11/2012 Computational Thinking is Informational Thinking 10

Computing Science? • NO! • we need to teach all students to think: – first characterise problem – then choose technology • how to characterise problems? 21/11/2012 Computational Thinking is Informational Thinking ! K N I TH 11

Computational thinking • modern approach to problem solving • draws heavily on insights from Computing Science • very widely applicable • not about programming http: //chomskyscolorlessgreenideas. blogspot. co. uk/2009/09/chinese-room. html 21/11/2012 Computational Thinking is Informational Thinking 12

Computing triangle Computational Thinking problems praxis solutions ICT concepts social needs tools 21/11/2012 Computational Thinking is Informational Thinking Computing Science 13

Computational thinking • homage to: – N. Wirth, Algorithms + Data Structures = Programs, Prentice-Hall, 1973 • information + computations = solutions – how do we know when the problem is solved? – what information is relevant to solving the problem? – how must the information change for the problem to be solved? – what computation(s) should we perform on the information to reach the solution? 21/11/2012 Computational Thinking is Informational Thinking 14

Computational thinking • hardest part is characterising the problem • two standard approaches: – ask someone else. . . – look for a similar problem you already know how to solve • what makes problems similar? – similar information + computations 21/11/2012 Computational Thinking is Informational Thinking 15

Computational thinking • four core techniques; – decomposition – pattern recognition – pattern generalisation/abstraction – algorithm design 21/11/2012 Computational Thinking is Informational Thinking 16

Decomposition • identify the information needed to solve problem • break the problem up into smaller subproblems • identify the subinformation needed to solve sub-problems 21/11/2012 http: //www. lostateminor. com/2012/03/14/if-stonehenge-came-with-ikeainstructions/ Computational Thinking is Informational Thinking 17

Pattern recognition • look for patterns amongst problems – have I seen a problem like this before? – how is this problem different? • look for patterns in the information http: //en. wikipedia. org/wiki/Bank_statement – how is the information structured? – are there useful relationships within the information? – have I seem information organised like this before? – how is this information different? http: //www. moneymatterstome. co. uk/interactiveworkshops/writingcheque. htm 21/11/2012 Computational Thinking is Informational Thinking 18

Pattern generalisation/abstraction • what is the general case for my problem? – what doesn’t change in how sub -problems are organised? – what does change? http: //en. wikipedia. org/wiki/Bank_statement • what is the general organisation of the information? – what doesn’t change in structure? – what information does change? http: //www. learndirect. co. uk/campaigns/worth-learning/managing -money/understanding-bank-statement/ 21/11/2012 Computational Thinking is Informational Thinking 19

Algorithm design • what is the sequence of steps from the initial information to the problem being solved? • how are sub-problems connected? • how does the information change between steps? 21/11/2012 Mug design – Le Chat Noir Boutique http: //www. lechatnoirboutique. com/proddetail. php? prod=FSFPTS Computational Thinking is Informational Thinking 20

Applying computational thinking • what does this mean for practical teaching? • where to begin? • are familiar approaches useful? • can we retrofit computational thinking to what we do already. . . ? 21/11/2012 An armillary sphere and discussion by R. W. Seale, in A New Geographical Dictionary, " published by J. Coote, London, 1760 *http: //www. columbia. edu/itc/mealac/pritchett/00 routesdata/17 00_1799/compendia/coote/sphere. jpg Computational Thinking is Informational 21 Thinking

Current approaches • programming language oriented – – – full strength language pedagogic or full strength subset functional/logic object oriented pseudo code – language independent • simple to complex – stepwise refinement – structured programming – iterative prototyping 21/11/2012 Computational Thinking is Informational Thinking 22

Current approaches • components – – – 21/11/2012 • design modular programming algorithms data structures types classes libraries – – flowcharts data flow entity relationship UML • • • Computational Thinking is Informational Thinking use case class diagrams structure diagrams sequence diagrams state machines 23

Current approaches fashions change too many possibilities none work easily beyond simple cases all have a strong focus on the final programming not hard when you know how to solve the problem. . . • . . . except for vagaries of specific language syntax & semantics & tools – get in the way of problem solving & model making • • • 21/11/2012 Computational Thinking is Informational Thinking 24

Forget the past. . . ! • familiar approaches useful – at end of problem solving to realise solution – with more advanced classes when basic computational concepts understood • need to focus on problem without worrying about how to implement solution • key is to characterise information & computations • start with information, not computations 21/11/2012 Computational Thinking is Informational Thinking 25

Characterising information • information = base/atomic elements + structure • NB not type, class etc – programming concepts – used to represent/implement problem information 21/11/2012 Computational Thinking is Informational Thinking 26

Characterising information • base/atomic elements – things! • at simplest, represented as symbols – sequences of characters – unitary entities that are meaningful – e. g. words, numbers • elements may be composite – made up of sub-elements – structured 21/11/2012 Computational Thinking is Informational Thinking 27

Characterising information • information structures – sequences: before/after, unordered/ordered – tables: rows & columns of contents – arrays: index & contents – record: fields & contents – lists: head & tail values – trees: value & branches – graphs: nodes & arcs • NB not data structures: programming concept 21/11/2012 Computational Thinking is Informational Thinking 28

Characterising information • structures have equivalences – table == array of records of row/column contents – array == list of index/value records – list == array of records of head & tail • for some problems, base elements in structures are themselves structures • where to begin. . . ? • back to Computing Science. . . ? 21/11/2012 Computational Thinking is Informational Thinking 29

Thinking about information • start in real world! • look at lots of concrete examples: – parked cars – numbered houses – supermarket queue – English v Scottish bank queue – shopping list 21/11/2012 – receipt – bill – account statement – itinerary – diary – calendar – invitation list – address book – seating diagram Computational Thinking is Informational Thinking 30

Thinking about information – shop catalogue – library catalogue – family tree – cladistic tree – decision tree – underground map – road map – lottery ticket – betting slip 21/11/2012 – sports league table – mobile contacts – browser favourites – social media friends – e. Book downloads – digital photo album – digital music library – music play list Computational Thinking is Informational Thinking 31

Thinking about information • use concrete problem scenarios: • typically, how to find specific information in structure • start from scratch • try to characterise scenario’s: – elements – structures • don’t yet introduce named structures • do this intuitively 21/11/2012 Computational Thinking is Informational Thinking 32

Thinking about information • ask if information is: – simple or composite – linear or grid or branching or cyclical – unordered or ordered – fixed or changeable: • content • size • shape 21/11/2012 Computational Thinking is Informational Thinking 33

Thinking about information • ask how to access elements – why do you want to access elements – which elements do you want to access – how does why/which affect access – where to start access – how to continue access – how to know if successful/unsuccessful – what to do if unsuccessful • then ask how to add/delete/modify elements 21/11/2012 Computational Thinking is Informational Thinking 34

Thinking about information • next, compare apparently disparate structures • ask what: – changes – stays the same • look for similarities in: – concrete detail – gross structure, ignoring elements – access/update/add/delete • use comparisons to draw out named structures 21/11/2012 Computational Thinking is Informational Thinking 35

Thinking about information • finally develop notion of abstract data type (ADT) • operations to: – construct new, empty structure – add to structure – find in structure – change contents – delete from structure 21/11/2012 Computational Thinking is Informational Thinking 36

From information to computation • abstract data type defines base operations – like the verbs of a language • computations structure these operations into algorithms • try to make structure of computation follow structure of information • not yet programming. . . 21/11/2012 Computational Thinking is Informational Thinking 37

From information to computation • go back to scenarios • computation solves problems by changing old information into new information • in the scenario, explore sequences of information change 21/11/2012 Computational Thinking is Informational Thinking 38

From information to computation • typically want to: – traverse structure • visit each element once • continue visit until some condition is met – do something to each element – accumulate intermediate information 21/11/2012 Computational Thinking is Informational Thinking 39

Structure traversal • bounded iteration – from first to last element – notion of next element • unbounded iteration – continues while some condition holds • how to start/continue/end iteration. . . ? 21/11/2012 Computational Thinking is Informational Thinking 40

Structure traversal • bounded recursion – base case • at end of structure so return final value – recursion case • do something with current element • recurse on rest of structure • how to start/end/continue recursion. . . ? 21/11/2012 Computational Thinking is Informational Thinking 41

Accumulate information • need to keep track of intermediate stages in computation – different positions in structure – partial results from computations • variable – general name/value association – change association with new value from computation, often using old value • use variable to manage stages of traversal. . . 21/11/2012 Computational Thinking is Informational Thinking 42

Do something to element • change element • how to identify element? – position in structure associated with a value – position often found via structure name – position is like a compound variable 21/11/2012 Computational Thinking is Informational Thinking 43

How to decide what to do • often want to change element regardless of its properties • may only want to change element if it satisfies certain criteria – choice/condition • may want to change what to do next depending on element properties • use choice to manage stages of traversal. . . 21/11/2012 Computational Thinking is Informational Thinking 44

Characterising the computation • finally explore if computation is necessarily: – iterative, or could it be recursive? – sequential, or could it be concurrent? – linear, or could it be backtracking? – deterministic, could it be non-deterministic? – bounded, or could it be unbounded? 21/11/2012 Computational Thinking is Informational Thinking 45

But. . . • need to describe information and computation in a consistent manner • so why not just use a programming language? – programming is motivating – seeing things work early on is motivating 21/11/2012 Computational Thinking is Informational Thinking 46

But. . . • so, use practical programming alongside problem solving! • but. . . – choice of language affects what can be described – programming language fine detail gets in way of fundamental concepts – simple small scale intuitive techniques don’t scale to realistic problems • for problem solving, use a neutral notation – e. g. pseudo code 21/11/2012 Computational Thinking is Informational Thinking 47

Conclusions • focus on problem solving not programming • CT = decomposition + abstraction + patterns + algorithms • solution = information + computation • let information structure the computation • start with concrete instances • use CT to: – ask good questions – find well kent information structures – guide computation design 21/11/2012 Computational Thinking is Informational Thinking 48

Conclusions • CT is a framework not a recipe • CT components overlap and interact • maybe classic CT overemphasises computation at expense of information? http: //affordablehousinginstitute. org/blogs/us/page/251 21/11/2012 Computational Thinking is Informational Thinking 49

Activity 1. choose two very different scenarios from the following list: – parked cars – numbered houses – supermarket queue – English v Scottish bank queue – shopping list – receipt 21/11/2012 – bill – account statement – itinerary – diary – calendar – invitation list – address book – seating diagram – shop catalogue Computational Thinking is Informational Thinking 50

Activity – library catalogue – family tree – cladistic tree – decision tree – underground map – road map – lottery ticket – betting slip 21/11/2012 – sports league table – mobile contacts – browser favourites – social media friends – e. Book downloads – digital photo album – digital music library – music play list Computational Thinking is Informational Thinking 51

Activity 2. choose one information structure from the following list: • sequence: before/after, unordered/ordered • table: rows & columns of contents • array: index & contents • record: fields & contents • list: head & tail values • tree: value & branches • graph: nodes & arcs 21/11/2012 3. explain how to represent both scenarios from 1. using the information structure from 2. This is well suited for use as an activity for a pair of people. Computational Thinking is Informational Thinking 52

Resources Jeanette Wing, `Computational Thinking’, Communications of the ACM, Vol 49, No. 3, pp 33 -35, March 2006 http: //www. cs. cmu. edu/~wing/publications/Wing 06. pdf Google, `Exploring Computational Thinking’ http: //www. google. com/edu/computational-thinking/ Pat Phillips, `Computational Thinking: A Problem Solving Tool for Every Classroom’ , CSTA/Microsoft http: //education. sdsc. edu/resources/Comp. Thinking. pdf 21/11/2012 Computational Thinking is Informational Thinking 53