Prerequisites and Educational Goals for Undergraduate Level e-Science Course(s)

This document contains output from the Curricula Development Workshop held in February 2008 in Brussels. The workshop first focused on developing interdisciplinary content for an undergraduate digital-systems/e-Science curriculum which would be introduced in three stages: e-Working, Basic Methods and Advanced Research Methods.

STAGE 1: e-Working

The Stage 1 course has been proposed as a requirement across disciplines, available to every student, whereas at present it appears selectively in certain disciplines. There are no hard and fast prerequisites, beyond general university prerequisites, but certain experience would be assumed, including previous use of email, a browser, chat client, word processor, PowerPoint and other software that would be part of a collaborative learning environment.

Stage 1 would be an introductory module imparting students with an understanding of digital-systems thinking, which would provide the basis for further education in e-Science. Educational goals to achieve by Stage 1 include using common communication tools in a professional manner, the ability to deal with complex tasks using process thinking (to organize work on tasks as an individual and in groups), and producing results from a team effort that properly reflect contributions and correctly cite material. Tools familiar to the students are used, but Stage 1 stresses the importance of learning how to work together effectively on tasks using these tools, emphasizing collaborative behaviour and provoking students to think about how they are using technologies; use of tools in a shared context involves new skills and presents ethical issues unique to that context. This stage promotes flexible thinking in students and learning through collaborative tasks.

Stages & Competencies

• Use email, web-search, word-processing, presentation tools with standards in mind, considering the quality of communication (how to use these tools responsibly—ethically, with proper citation and accuracy)
• Develop team working using the above
• Use digital-communication tools (work with libraries)
  • To coordinate & develop a deliverable
  • Responsibility, legal, ethical & social issues
  • Security and safety
• Critical thinking
• Use of subject-specific digital resources
  • Scientific data and document data
  • Metadata and controlled vocabularies
  • Proper citation and legitimate use
  • IPR
• Collaborative behaviour
  • Plagiarism and its detection
  • Drawing on strengths & knowledge of team members
  • Strategies for dealing with weaknesses and lack of knowledge

An example of a task assigned to teach these skills could be collaboration on a written report. First students in a group would need to decide who obtains what material for the report (distribution of work, location of resources, how to best access resources). Once they find the resources, they then must consider what should be extracted and how it should be organised, the proper way to cite references, and how to present the report as a team in a coherent manner. To do this, each student has to think about breaking the task down and then reassembling material, thus learning process thinking. The emphasis at Stage 1, as previously stated, would be on working effectively in a collaborative environment using information and communication technologies.

STAGE 2: Basic Methods

Stage 2 will continue to teach competencies introduced in Stage 1, but strands will be tailored to specific disciplines. While aspiring to make the Stage 2 course a requirement, it was decided that it would initially be proposed as optional and with time may naturally become compulsory. Completion of Stage 1 would be the prerequisite for entering Stage 2. As with Stage 1, Stage 2 content remains at a conceptual level and provides students with a mental model of tools for e-Science.

Critical thinking (2)*

• Data management*
  • Data lifecycle, Data Bases, Data models, Metadata, Mark up languages
  • Controlled vocabularies, Ontologies
  • Information entropy
    • Compression
• Complexity
  • Origins of real-world & system complexity
  • Handling complexity
• Use of models*
  • Validity and domains of applicability
  • Data dependence and interpretation of results
• Numerical thinking
  • Nature & origins of error
  • Precision, correctness & validation
  • Types of numbers and their behaviour / representation
• Statistical thinking
  • Sampling and error
  • Uses and abuses of statistics
• Using multiple data resources
  • Semantic and provenance

Responsibility, legal, ethical & social issues (2)*

Presentation and interpretation of data

• Visualisation
• Interdisciplinary professional communication

Image analysis

• Derivation of information

Process thinking

Logical thinking and decidability

Trust: security, privacy and integrity*

• Risk and impact
• Implementations, their strengths and weaknesses (not core)
• Personal behaviour

Distributed systems thinking

• Digital communication and network services
• Distributed systems architectures
• Storage systems and preservation
• Instrumentation
  • Digital devices, sensors and networks

How much of distributed systems are discussed depends on your audience. For this undergraduate level, it is more important here to recognize that there are models behind commonly used tools such as Facebook and Google. Google, for instance, would provide students with a rough idea of documents on a certain subject, but if you want to find all the references relating to a particular topic or the definitive article on that topic, you would not use this tool (you could contrast Google with a citation index). It would not be necessary to talk about the Baysian model, but instead focus students attention on the results associated with using such a model (the answers you get). The student would learn that models are tuned to provide certain results, so it is important to recognize what the model is meant to do (what it reveals and what is left out).

Generic Property of Stage 2:

• Everyone is aware of
  • the terms and their meaning
  • Where to find experts & more information
• If we were to get someone from Stage 2
  • & set up a tool for them
  • Then they can quickly learn to use the tool
  • They can engage in informed dialogue about their digital-systems uses and requirements

(Starred bullets are considered core elements)

STAGE 3: Advanced Research Methods

Stage 3 equips students to choose, configure, parameterise and compose tools in e-Science. As with Stage 2, this stage will be considered optional. Stages 1 and 2 are prerequisites for entrance into Stage 3. This level is domain-specific and involves exposure to a range of tools and to programming.

• Expect experts in a narrower space as a result of this level
  • This level is typically very domain specific
• Can choose, configure, parameterise and compose tools
• Able to engage with developers in specifying and evaluating tools
• Depending on the subject:
  • This may include middleware, services & applications
• Generic tools may be part of courses here
  • E.g. portal / problem solving environments
  • Workflows
  • Grid computing, HTC & (optionally) HPC
  • Concurrency, parallelism & computing architectures
  • Large-scale storage technology
  • High-bandwidth communications