Formats and tools
- Unit Description
- Reconstruct the unit from the xml and display it as an HTML page.
- Assessment Tool
- an assessor resource that builds a framework for writing an assessment tool
- Assessment Template
- generate a spreadsheet for marking this unit in a classroom environment. Put student names in the top row and check them off as they demonstrate competenece for each of the unit's elements and performance criteria.
- Assessment Matrix
- a slightly different format than the assessment template. A spreadsheet with unit names, elements and performance criteria in separate columns. Put assessment names in column headings to track which performance criteria each one covers. Good for ensuring that you've covered every one of the performance criteria with your assessment instrument (all assessement tools together).
- Wiki Markup
- mark up the unit in a wiki markup codes, ready to copy and paste into a wiki page. The output will work in most wikis but is designed to work particularly well as a Wikiversity learning project.
- Evidence Guide
- create an evidence guide for workplace assessment and RPL applicants
- Competency Mapping Template
- Unit of Competency Mapping – Information for Teachers/Assessors – Information for Learners. A template for developing assessments for a unit, which will help you to create valid, fair and reliable assessments for the unit, ready to give to trainers and students
- Observation Checklist
- create an observation checklist for workplace assessment and RPL applicants. This is similar to the evidence guide above, but a little shorter and friendlier on your printer. You will also need to create a seperate Assessor Marking Guide for guidelines on gathering evidence and a list of key points for each activity observed using the unit's range statement, required skills and evidence required (see the unit's html page for details)
- Self Assessment Survey
- A form for students to assess thier current skill levels against each of the unit's performance criteria. Cut and paste into a web document or print and distribute in hard copy.
- Moodle Outcomes
- Create a csv file of the unit's performance criteria to import into a moodle course as outcomes, ready to associate with each of your assignments. Here's a quick 'how to' for importing these into moodle 2.x
- Registered Training Organisations
- Trying to find someone to train or assess you? This link lists all the RTOs that are currently registered to deliver BSBMKG528, 'Mine data to identify industry directions'.
- Google Links
- links to google searches, with filtering in place to maximise the usefulness of the returned results
- Books
- Reference books for 'Mine data to identify industry directions' on fishpond.com.au. This online store has a huge range of books, pretty reasonable prices, free delivery in Australia *and* they give a small commission to ntisthis.com for every purchase, so go nuts :)
Elements and Performance Criteria
ELEMENT | PERFORMANCE CRITERIA |
Elements describe the essential outcomes. | Performance criteria describe the performance needed to demonstrate achievement of the element. |
1. Determine purpose of data mining | 1.1 Identify and review relevant client or organisational requirements for data mining 1.2 Confirm potential uses of data mining outcomes and recommendations 1.3 Recognise privacy and other requirements within current legislation, regulation and organisational policy that impact on data mining activities |
2. Identify data sources | 2.1 Identify available data sources from public, client and organisation systems 2.2 Negotiate access rights and intellectual property release for relevant data sources 2.3 Rank and prioritise data sources for validity, reliability and completion rates |
3. Apply data mining techniques | 3.1 Select appropriate tools and techniques suitable for the type and expected degree of complexity in data analysis 3.2 Classify data according to relevant factors including type, content, relationships, location, demographics and maturity 3.3 Analyse data to identify patterns, clusters and relationships 3.4 Use suitable graphical tools to visualise aggregated data |
4. Report and recommend on findings | 4.1 Assess results of data mining against requirements in order to draw relevant insights 4.2 Weight insights for reliability and validity 4.3 Report data mining process and outcomes in suitable format to support the organisation's knowledge base 4.4 Document lessons learned during the processes for future use |