91636 Sample Specific Assessment Guide · Web vieware systems that exhibit aspects of human intelligence in their interaction with their users or environment. Engineering such systems

Level 3 Digital Technologies 91636 (3.44)

Specific Assessment Guides

Complexity and Tractability: 1–4Formal Languages: 5–8

Graphics and Visual Computing: 9–12Intelligent Systems: 13–16

Network Communication Protocols: 17–20Software Engineering: 21–24

Level 3 Digital Technologies 91636 (3.44) Specific Assessment Guide (Complexity and Tractability) — page 0 of 24

Level 3 Digital Technologies 91636 (3.44) Specific Assessment Guide(Complexity and Tractability)

Title Demonstrate understanding of areas of computer science

Credits 4

Teacher introduction

Technology assessment guides have been produced to help teachers develop their own specific assessment guides. Examples of specific assessment guides, developed from the common assessment guide for each standard, have been produced as part of the external assessment resources for level 3 Technology.

The specific assessment guides also show a variety of ways (ie case study, research, practice) to produce external assessment material. The material in the candidate exemplars for each standard reflects the content and context of the specific assessment guides.

Teachers can adapt a common assessment guide and / or a specific assessment guide to suit the specific context of their course of teaching.

Candidate introduction

You will produce a report that demonstrates understanding of areas of computer science. To complete the report you will need to report on at least two of the Areas of Computer Science from explanatory note 3 in the standard.

This specific assessment guide is one of six. Each one of the specific assessment guides relate to one of the six Areas of Computer Science.

Candidate guidance for producing the report

There are some prompts and activities below that will assist you to write the part of your report on complexity and tractability. They will help you to produce a report that demonstrates the understanding expected in this assessment. The prompts also define the levels of description, explanation, and discussion that are expected at each grade.

To demonstrate understanding of areas of computer science at the Achieved level you will need to:

describe key problems that are addressed in selected areas of computer science describe examples of practical applications of selected areas to demonstrate the use of key

algorithms and/or techniques from these areas.

To demonstrate in-depth understanding of areas of computer science at the Merit level you will need to:

explain how key algorithms or techniques are applied in selected areas explain examples of practical applications of selected areas to demonstrate the use of key algorithms

and/or techniques from these areas.


To demonstrate comprehensive understanding of areas of computer science at the Excellence level you will need to:

discuss examples of practical applications of selected areas to demonstrate the use of key algorithms and/or techniques from these areas

evaluate the effectiveness of algorithms, techniques, or applications from selected areas.

Possible Activities

The activities below are activities which generate specific content that you can use to develop your report. For example, if you were to investigate TSP (see activity 6 below) you could generate information related to several parts of the report.

1. Evaluate how much time is needed to solve the TSP, and evaluate approximate solutions (such as nearest neighbour first).

2. Investigate the graph colouring problem; a sample plan for this is at Computing Inside's Graph Colouring Activity, as well as the CS Unplugged's map colouring activity. It can be done online at http://gwydir.demon.co.uk/jo/games/puzzles/map.htm.

3. Investigate the (intractable) Travelling Rock Band problem.

4. Investigate the knapsack problem, and evaluate approximate solutions (such as decreasing order).

5. Investigate the bin-packing problem, and evaluate approximate solutions (such as the first-fit algorithm).

6. Investigate the progress researchers have made over the years finding improvements to algorithms for solving a particular problem (eg TSP, primality testing).

7. Compare different algorithms with a variety of complexities for the same problem, particularly for large values of $n$, considering issues like how well they scale and how much memory they use.

8. Compare the speed of ‘bogosort’ (an exponential time algorithm) with a conventional sorting algorithm (such as ‘quicksort’).

9. Estimate the work involved for a computer to evaluate all possible timetabling options for your school.

Essential documents

The achievement standard governing this specific assessment guide can be found athttp://www.nzqa.govt.nz/nqfdocs/ncea-resource/specifications/2013/level3/91636-spc-2013.pdf

The assessment specifications for the Digital Technologies achievement standard can be found athttp://www.nzqa.govt.nz/nqfdocs/ncea-resource/achievements/2013/as91636.pdf

http://www.nzqa.govt.nz/nqfdocs/ncea-resource/achievements/2013/as91636.pdf

http://www.nzqa.govt.nz/nqfdocs/ncea-resource/specifications/2013/level3/91636-spc-2013.pdf

http://www.colorado.edu/education/DMP/activities/graph/jaeact01.html

http://gwydir.demon.co.uk/jo/games/puzzles/map.htm

http://csunplugged.org/graph-colouring

http://csi.dcs.gla.ac.uk/workshop-view.php?workshopID=6



Definitions

The following are concepts, algorithms, techniques, applications, and problems that students at level 3 are likely to be able to work with; it is not a list of all the key ideas in the area.

Key concepts likely to be encountered are: complexity, exponential time complexity, polynomial time complexity, tractability, asymptotic complexity, big O notation, P and NP, and NP-complete problems.

Algorithms: there are many hundreds of algorithms that illustrate these issues; suitable intractable problems include the travelling salesperson problem (TSP), Hamiltonian path, graph colouring, vertex cover, Sudoku, and longest path; contrasting tractable problems include the Eulerian path, minimal spanning tree, and shortest path. Standard sorting or searching algorithms are excellent for exploring the concept of complexity; bogosort and bozosort can be explored as (artificial) examples of intractable algorithms.

Techniques: empirical evaluation, analysis, brute force algorithm, heuristic algorithms.

Applications: these include route planning, timetabling, optimisation, games, and encryption.

Complexity and tractability is about the relationship between problems and their algorithms, and the idea that some common problems do not have tractable solutions. This falls in the area of computational complexity theory. The focus is on the inherent complexity of a problem, that is, the time needed to solve a problem, and the best known algorithms for the problem. This area includes what is widely regarded as the largest unsolved problem in computer science: the question of whether P = NP (the details of this issue are beyond high school level, but the explorations that can be performed at high school level will give an understanding of why this is such a significant problem). The demonstration of understanding in this area can be done by describing problems with known inherent complexities (both tractable and intractable) and those for which the complexity is an open question; by illustrating the issues surrounding intractable (exponential time) algorithms; by exploring the limits on what can be done with ‘intractable’ problems (such as the various records that have been set for solving the TSP); by comparing heuristic solutions that give sub-optimal solutions; and by exploring the quest to find reasonable time algorithms for those that currently only have exponential time solutions, including recent discoveries about open questions in this area.

Further informationUseful links:

http://en.wikipedia.org/wiki/Computational_complexity_theory http://www.tsp.gatech.edu/games/index.html http://csunplugged.org/graph-colouring http://en.wikipedia.org/wiki/Travelling_salesman_problem http://en.wikipedia.org/wiki/Knapsack_problem http://en.wikipedia.org/wiki/Bin_packing_problem http://en.wikipedia.org/wiki/Hamiltonian_path http://en.wikipedia.org/wiki/Brute-force_search

Further information can be found at http://www.techlink.org.nz .

Exemplars

Please read the exemplars. You can model your work on these exemplars but you may not copy the material from the exemplars. Your report must be the product of your own efforts.

http://www.techlink.org.nz/

http://en.wikipedia.org/wiki/Brute-force_search

http://en.wikipedia.org/wiki/Hamiltonian_path

http://en.wikipedia.org/wiki/Bin_packing_problem

http://en.wikipedia.org/wiki/Knapsack_problem

http://en.wikipedia.org/wiki/Travelling_salesman_problem

http://csunplugged.org/graph-colouring

http://www.tsp.gatech.edu/games/index.html

http://en.wikipedia.org/wiki/Computational_complexity_theory


Schedule

Assessment Schedule

AS Digital Technologies 91636 (3.44)

Demonstrate understanding of areas of computer science

Final grades will be decided using professional judgement based on a holistic examination of the evidence provided against the criteria.

Issues from the Specifications

Authentic candidate submissions will be recognisable because of specific contexts associated with the work. This does not imply that submissions will arise only from the candidate’s practice. However, where the candidate’s practice does not provide the immediate source of a specific context, one would expect to see that several sources of information relating to materials had been applied within a specific context. In both cases, the marker will be able to detect the candidate’s voice. In situations where information does not have some aspect of student voice, it is difficult to establish whether the candidate has actually demonstrated understanding or simply identified information.

Candidates who have simply identified information by reproducing information from sources without making use of that information have not demonstrated understanding.

Where a candidate has provided a brief answer, the answer should not be penalised because of length.

Candidate work in excess of 14 pages should not be marked.

Where work is illegible, it cannot be marked.

Digital submissions that cannot be read cannot be marked.

Achievement Achievement with Merit Achievement with Excellence

Demonstrating understanding of areas of computer science involves:

Demonstrating in-depth understanding of areas of computer science involves:

Demonstrating comprehensive understanding of areas of computer science involves:

describing key problems that are addressed in selected areas of computer science

describing examples of practical applications of selected areas to demonstrate the use of key algorithms and/or techniques from the.se areas.

explaining how key algorithms or techniques are applied in selected areas

explaining examples of practical applications of selected areas to demonstrate the use of key algorithms and/or techniques from these areas.

discussing examples of practical applications of selected areas to demonstrate the use of key algorithms and/or techniques from these areas

evaluating the effectiveness of algorithms, techniques, or applications from selected areas.

Level 3 Digital Technologies 91636 (3.44) Specific Assessment Guide


(Formal Languages)


Credits 4









There are some prompts and activities below that will assist you to write the part of your report on formal languages. They will help you to produce a report that demonstrates the understanding expected in this assessment. The prompts also define the levels of description, explanation, and discussion that are discussion that are expected at each grade.











Possible Activities

The activities below are activities which generate specific content that you can use to develop your report. For example, if you were to show how a sample program could be parsed using a grammar you could generate information related to several parts of the report.

1. Demonstrate how compilers, interpreters, parsers, or validators find errors in formal languages, eg introduce an error to a compiled program, XML document file, or web page, and show the effect of the error.

2. Explore regular expressions for pattern matching using a system such as the ‘Regex dictionary’ at http://www.visca.com/regexdict/, the Microsoft Word Find command with wildcards, regular expressions available in a language such as Java (java.util.regex), JavaScript (RegExp), or Python (‘re’ module), or the ‘grep’ program, to find words in an English dictionary that match a pattern.

3. Find a grammar for a simple arithmetic expression in a programming language, and show the parse tree for sample expressions (such as (a+b)*(c-d)).

4. Make up a few simple Regular expressions (such as ab*a), create a (non-deterministic) finite state automaton for each one using a tool like JFLAP, and show the operation of the automaton for sample strings (aa, aba, abba, etc in the case of the example).

5. Write regular expressions to find patterns in documents, such as a URL, email address, date, or time.

6. Find a grammar for a programming language, and show how a sample program would be parsed using the grammar.

7. Explore the grammar for balanced parentheses S SS, S (S), S ( ).

8. Explore using a tool such as Lex, Yacc, Flex, or Bison to parse a simple language.

9. Generate art using the ‘context free art’ system (http://www.contextfreeart.org/).

Essential documents



Definitions



http://www.contextfreeart.org/

http://www.visca.com/regexdict/



Key concepts that are likely to be encountered are: string, grammar, parsing, parse tree, syntax, syntactically correct, regular expression, finite state automaton, lexical analysis, and Chomsky hierarchy.

Algorithms: algorithms associated with formal languages are beyond the scope of this level, although using ad-hoc solutions such as drawing a parse tree will be appropriate.

Techniques: syntax diagrams, grammars, parse trees, finite state automata (both representing and executing), regular expressions, pattern matching.

Applications: compilers, interpreters, text processing, language specification, HTML viewer.

Formal languages is about how to specify programming, markup, and other languages for computing, and systems that can parse and process programs or documents written in such languages. They are specified by formal representations such as syntax diagrams (‘railroad diagrams’), grammars, regular expressions, and finite state machines. The language could be a conventional programming language (such as Java, Python, C, or Basic), another formal language with a strict syntax (such as XML, HTML, or SQL), or the focus could be on regular expressions and lexical analysis (such as detecting a well-formed identifier or number in a programming language, or a string matching a given pattern). Most programming languages have very large formal definitions, and it would be sufficient to demonstrate understanding using a part of a language, such as expressions in a programming language, or a small selection of different kinds of tags in HTML. The demonstration would typically be done by using examples to show the parse tree (or syntax tree) for a correct and incorrect program fragment, or to show a sequence of grammar productions to construct a correct program fragment, or to show strings generated by a simple regular expression and accepted by a finite state machine that corresponds to it.

Further information

Useful links:

http://en.wikipedia.org/wiki/Formal_language http://en.wikipedia.org/wiki/Context-free_grammar#Examples http://en.wikipedia.org/wiki/Abstract_syntax_tree http://en.wikipedia.org/wiki/Regular_expression http://www.netcraftsmen.net/presos/Regex_Practice/player.html http://csunplugged.org/finite-state-automata http://word.mvps.org/FAQs/General/UsingWildcards.htm http://www.i-programmer.info/babbages-bag/223-finite-state-machines.html http://www.jflap.org/


Exemplars


Schedule


http://www.jflap.org/

http://www.i-programmer.info/babbages-bag/223-finite-state-machines.html

http://word.mvps.org/FAQs/General/UsingWildcards.htm

http://csunplugged.org/finite-state-automata

http://www.netcraftsmen.net/presos/Regex_Practice/player.html

http://en.wikipedia.org/wiki/Regular_expression

http://en.wikipedia.org/wiki/Abstract_syntax_tree

http://en.wikipedia.org/wiki/Context-free_grammar#Examples

http://en.wikipedia.org/wiki/Formal_language


Assessment Schedule





















Level 3 Digital Technologies 91636 (3.44) Specific Assessment Guide(Graphics and Visual Computing)



Credits 4









There are some prompts and activities below that will assist you to write the part of your report on graphics and visual computing. They will help you to produce a report that demonstrates the understanding expected in this assessment. The prompts also define the levels of description, explanation, and discussion that are expected at each grade.











Possible Activities

The activities below are activities which generate specific content that you can use to develop your report. For example, if you were to Show how lines and circles are drawn using Bresenham’s algorithm (see activity 1 below) you could generate information related to several parts of the report.

10. Show how lines and circles are drawn using Bresenham’s algorithm (see the ‘Drawing lines with pixels’ activity at http://csunplugged.org).

11. Define and render a scene using provided ray-tracing software, eg POV-Ray (http://library.thinkquest.org/3285/).

12. Evaluate the robustness of bar code or QR code reading software with different levels of distortion in the image.

13. Evaluate the effectiveness of facial recognition in free software such as Google’s Picasa.

14. Evaluate the Canny edge detection algorithm on your own images using the online processor at http://matlabserver.cs.rug.nl/.

15. Give examples of simple template matching on characters in a scanned document.

16. Evaluate the effectiveness of software for image stitching.

17. Perform scene modelling using images (image-based rendering, used in arcade games).

18. Explore modelling surfaces using splines, surfaces of revolution, and simple methods to generate terrain models.

19. Explore computational geometry methods (such as convex hulls and closest pair of points).

20. Explore 3D volume visualisation (generating a 3D view from slices), such as in MRI and HRCT.

Essential documents



Definitions




http://matlabserver.cs.rug.nl/

http://library.thinkquest.org/3285/

http://csunplugged.org/


Algorithms: Bresenham’s algorithm (line and circle drawing), colour space conversion, line anti-aliasing, Bézier and B-spline curves, painter’s algorithm, Z-buffer, sharpening filter, edge detection (Canny, Sobel), template matching, morphological functions.

Techniques: ray tracing, texture mapping, shading, anti-aliasing, volume rendering, polygonisation, constructive solid geometry, 3D modelling, hidden object removal, face recognition, object recognition.

Applications: drawing software, animation, image enhancement, barcode readers, face recognition, image stitching, collision avoidance, biometric authentication, surveillance, optical character recognition, medical imaging.

Graphics and visual computing is about using computers to create images and animations based on a description of a scene or collected data (computer graphics and visualisation), and the reverse process of processing images and recognising elements in an image (computer vision). Often the term ‘visual computing’ encompasses computer graphics, so the term ‘graphics’ isn’t strictly required in the name of this area; also note that in this context it does not refer to the use of ‘visual programming languages’ or ‘visual programming environments’ eg Visual Studio. The creation of images could be as simple as a 2D drawing program, or as advanced as 3D systems for entertainment or to help visualise a data set. Computer vision is used to capture information from the real world or to recognise situations such as a potential vehicle collision. These topics can be explored by evaluating the effectiveness of existing software for these purposes, and exploring algorithms and techniques for rendering images and recognising the contents of an image. This topic is distinct from the standards that explore the use of multimedia software as it explores the details of how that software works.

Further information

Useful links:

http://en.wikipedia.org/wiki/Computer_graphics http://en.wikipedia.org/wiki/Computer_vision http://en.wikipedia.org/wiki/Bresenham’s_line_algorithm http://en.wikipedia.org/wiki/Ray_trace http://en.wikipedia.org/wiki/Mri http://www.cosc.canterbury.ac.nz/mukundan/cogr/applcogr.html http://www.cosc.canterbury.ac.nz/mukundan/covn/applcovn.html http://www.povray.org/resources/links/3D_Tutorials/POV-Ray_Tutorials/


Exemplars


Schedule


http://www.povray.org/resources/links/3D_Tutorials/POV-Ray_Tutorials/

http://www.cosc.canterbury.ac.nz/mukundan/covn/applcovn.html

http://www.cosc.canterbury.ac.nz/mukundan/cogr/applcogr.html

http://en.wikipedia.org/wiki/Mri

http://en.wikipedia.org/wiki/Ray_trace

http://en.wikipedia.org/wiki/Bresenham's_line_algorithm

http://en.wikipedia.org/wiki/Computer_vision

http://en.wikipedia.org/wiki/Computer_graphics


Assessment Schedule























(Intelligent Systems)


Credits 4









There are some prompts and activities below that will assist you to write the part of your report on intelligent systems. They will help you to produce a report that demonstrates the understanding expected in this assessment. The prompts also define the levels of description, explanation, and discussion that are expected at each grade.











Possible Activities

The activities below are activities which generate specific content that you can use to develop your report. For example, if you were to Investigate the problem of developing a self-driving car (see activity 7 below) you could generate information related to several parts of the report.

1. Explore algorithms for playing simple board games such as noughts and crosses or checkers.

2. Develop a decision tree for a diagnosis or advice system.

3. Evaluate an online chatbot.

4. Explore an online automatic translation system, such as Google’s statistical translator.

5. Do the CS Inside predictive text learning exercise (http://csi.dcs.gla.ac.uk/workshop-view.php?workshopID=7).

6. Collect statistical data and use a simple machine learning system to find patterns in it.

7. Investigate the problem of developing a self-driving car (http://www.nsf.gov/cise/csbytes/newsletter/vol1i3.html).

8. Investigate how an AI system could win the Jeopardy game (http://www.nsf.gov/cise/csbytes/newsletter/vol1i10.html).

9. Experiment with an artificial life simulator.

10. Experiment with training a simple artificial neural net.

Essential documents



Definitions


Key concepts that are likely to be encountered are: Artificial Intelligence (AI), Turing test, strong AI and weak AI, machine learning, natural language processing, chatbot, singularity, commonsense knowledge, machine perception (vision, speech recognition), and biologically inspired algorithms.

Algorithms: search (eg best first search), game-tree (min-max) search, probability based mechanisms, decision trees, pattern matching, agglomerative clustering.



http://www.nsf.gov/cise/csbytes/newsletter/vol1i10.html

http://www.nsf.gov/cise/csbytes/newsletter/vol1i3.html




Techniques: logic, knowledge representation, search, heuristics, machine learning, artificial neural networks, genetic algorithms, probability, Bayesian probability, fuzzy logic, intelligent agents, artificial life.

Applications: games, classification, expert systems, natural language processing, machine translation, data mining, information retrieval, image processing, computer vision, robotics, software agents (eg chatbots).

Intelligent systems are systems that exhibit aspects of human intelligence in their interaction with their users or environment. Engineering such systems and the study of theoretical and practical issues surrounding them is the subject of the field of artificial intelligence (AI). AI is primarily a branch of computer science but it has borrowed a lot of concepts and ideas from other fields, especially mathematics (particularly logic, combinatorics, statistics, probability, and optimisation theory), biology, psychology, neuroscience, and philosophy. This area can be explored by experimenting with existing AI systems, such as online chatbots, decision systems, machine learning systems, search engines, machine translation, spam detectors, video game bots, and object recognition (eg face detection) systems. There is a lot of opportunity for exploring predictions and ethical debates regarding intelligent systems such as the concept of the singularity, Moravec’s Paradox, Searle’s Chinese Room, and the value of the Turing test; these have some bearing on the concepts in this standard, but could be explored as part of a separate generic standard relating to ethics or the effect of technology on society.

Further information

Useful links:

http://en.wikipedia.org/wiki/Outline_of_artificial_intelligence http://en.wikipedia.org/wiki/Turing_test http://en.wikipedia.org/wiki/Machine_learning http://www.cs4fn.org/ai/meetthechatterbots.php http://www.alicebot.org/ http://spectrum.ieee.org/robotics/artificial-intelligence http://www.ted.com/conversations/topics/artificial+intelligence http://www.cs4fn.org/ai/illusionintelligence.php


Exemplars


Schedule


http://www.cs4fn.org/ai/illusionintelligence.php

http://www.ted.com/conversations/topics/artificial+intelligence

http://spectrum.ieee.org/robotics/artificial-intelligence

http://www.alicebot.org/

http://www.cs4fn.org/ai/meetthechatterbots.php

http://en.wikipedia.org/wiki/Machine_learning

http://en.wikipedia.org/wiki/Turing_test

http://en.wikipedia.org/wiki/Outline_of_artificial_intelligence


Assessment Schedule























(Network Communication Protocols)


Credits 4









There are some prompts and activities below that will assist you to write the part of your report on network communication protocols. The prompts will help you to produce a report that demonstrates the understanding expected in this assessment. The prompts also define the levels of description, explanation, and discussion that are expected at each grade.











Possible Activities

The activities below are activities which generate specific content that you can use to develop your report. For example, if you were to Use tools like traceroute to evaluate the time taken and number of hops required for packets to be transmitted through the internet (see activity 1 below) you could generate information related to several parts of the report.

21. Use tools like traceroute to evaluate the time taken and number of hops required for packets to be transmitted through the internet.

22. Using packet analyser software (eg wireshark), trace the sequence of exchanges that occur for a protocol such as DNS, UDP, or HTTP using an example on your own system.

23. Participate in and document the ‘tablets of stone’ activity (http://csi.dcs.gla.ac.uk/workshop-view.php?workshopID=4).

24. Explain how HTTP provides the foundation of the world wide web, how IP solves the problem of individually addressing the thousands of computers that comprise the internet, and / or how DNS makes the internet friendly for human users.

25. Use tools to show how encryption algorithms and security protocols can provide confidentiality, integrity, and availability of information exchanged between parties.

Essential documents



Definitions


Key concepts likely to be encountered are: addressing, reliability, security, failure, packet loss, human-readable addresses, quality of service, network performance, cyber attacks, and routing.

Algorithms: (techniques are more relevant to this area than algorithms)

Techniques: packet switching, handshaking, acknowledgement, authentication, checksums, wireless and wired security.

Applications: many real protocols (eg TCP/IP) could rapidly become overwhelming at this level. Protocols that students could investigate that are less complex are DNS, UDP, HTTP (get and post), the addressing part of IP, SMTP and CDMA, and internet security protocols such as SSL, IPSec, and PGP.

Network communication protocols focus on the techniques applied in computer networks to ensure reliable communication of data between two parts of a network in the face of different kinds of threats and failures. The project would typically be done by giving examples of the sequence of events that






occur in these situations, discussing how the protocols and their coding schemes overcome the problems, and evaluating how successful they are at addressing them. This topic is distinct from the coverage of networking in the infrastructure standards because it focuses on the issues that the protocols address (ie the design of the protocol), rather than how to configure a system that uses a given protocol.

Further information

Useful links:

http://en.wikipedia.org/wiki/Network_protocol

http://www.w3schools.com/tcpip/

http://computer.howstuffworks.com/internet/basics


http://www.cbtnuggets.com/ (Cisco CCENT ICND1 640-822 series – free preview but fee for full access)

http://en.wikipedia.org/wiki/Cryptographic_protocol



Exemplars


Schedule



http://en.wikipedia.org/wiki/Cryptographic_protocol

http://www.cbtnuggets.com/


http://computer.howstuffworks.com/internet/basics

http://www.w3schools.com/tcpip/

http://en.wikipedia.org/wiki/Network_protocol


Assessment Schedule























(Software Engineering)


Credits 4







This specific assessment guide is one of six. Each one of the specific assessment guides relates to one of the six Areas of Computer Science.


There are some prompts and activities below that will assist you to write the part of your report on software engineering. They will help you to produce a report that demonstrates the understanding expected in this assessment. The prompts also define the levels of description, explanation, and discussion that are expected at each grade.











Possible Activities

The activities below are activities which generate specific content that you can use to develop your report. For example, if you were to Review what went wrong in a software disaster (see activity 3 below) you could generate information related to several parts of the report.

1. Interview practicing software engineer(s) about the techniques they use and the problems they encounter in their job, contrasting a plan-driven methodology with an agile methodology.

2. Play the SimSE game (http://www.ics.uci.edu/~emilyo/SimSE/) using the waterfall and Extreme Programming approaches, and report on what happened during the experience, contrasting the two approaches.

3. Review what went wrong in a software disaster.

4. Report on the viability of a career in software engineering (eg demand for software engineers).

5. Report on an experience doing a team activity that highlights the importance of communication and planning in a team working to create a product within constraints (eg building a house of cards or completing the 48-hour film competition), carefully following the role of each person, how they knew what to do, how effective the allocation of work was, and whether the desired outcome was achieved.

Essential documents



Definitions


Key concepts likely to be encountered are: plan-driven development, agile development, test-driven development, waterfall model, scrum, customer requirements, open source, object-oriented design, Brooks’s law, software crisis, software metrics, interpersonal communication, working environments, and outsourcing.

Algorithms: (techniques are more relevant to this area than algorithms)

Techniques: plan-driven development, agile development, test-driven development, scrum.

Applications: any large software project is an application of this topic. There is much information available publicly about large software projects (including some significant disasters), or students could interview someone involved in a current large project.



http://www.ics.uci.edu/~emilyo/SimSE/


Software engineering is about systematic approaches applied to large software projects, typically with many team members and large amounts of program code, so that the products behave reliably and efficiently, are affordable to develop and maintain, and satisfy customer requirements. This area can be explored by learning about common software engineering methodologies (including examples of ‘plan-driven’ and ‘agile’ approaches) and the different roles and skills required in a software engineering project, particularly analysis, development, testing, and maintenance. Understanding can be demonstrated by doing case studies of software projects through interviews with software engineers, researching reports about successful and unsuccessful projects, running a simulation of a software engineering project, or reflecting on teamwork experiences that simulate the issues that arise in software engineering. The report should discuss the main issues and compare different approaches in the context of commercial projects that involve multiple team members. This topic is distinct from the programming standards because it explores large systems developed by teams of people; participating in such a project is way beyond the scope of Level 3 work, and the expectation is that students will review commercial or simulated projects, rather than run one themselves.

Further information

Useful links:

http://en.wikipedia.org/wiki/Software_engineering http://en.wikipedia.org/wiki/Agile_software_development http://spectrum.ieee.org/computing/software/why-software-fails http://www.cs4fn.org/fundamentals/softwareeng.php http://www.teach-ict.com/as_a2_ict_new/ocr/A2_G063/331_systems_cycle/slc_stages/home_slc.html


Exemplars


Schedule


http://www.teach-ict.com/as_a2_ict_new/ocr/A2_G063/331_systems_cycle/slc_stages/home_slc.html

http://www.cs4fn.org/fundamentals/softwareeng.php

http://spectrum.ieee.org/computing/software/why-software-fails

http://en.wikipedia.org/wiki/Agile_software_development

http://en.wikipedia.org/wiki/Software_engineering


Assessment Schedule





















Documents

91636 Sample Specific Assessment Guide · Web vieware systems that exhibit aspects of human intelligence in their interaction with their users or environment. Engineering such systems