A profile of learners in an IPT class

The last post was the first step in designing a unit of work for a senior IPT (Information and Processing Technology) course as part of an assignment for a course titled ICTs for Learning Design. The intent is to show an ability to integrate e-learning into learning design in effective ways. The first part of the assignment requires a profile of the learners. The following is my first attempt at such a profile. I’m very interested to hear comments from those folk who are currently teaching IPT. Am I on the right track? What have I missed?

Learning Management Questions: 1, 2, and 3

The course and program I am studying is based on the concept of “learning management” which has a technique called the Learning Design Process which is based on 8 Learning Management Questions that

that organises ‘information’ required of learning managers for the successful sequencing, pacing and importantly delivery of curriculum material for individual learners.

The notion of the teacher having to sequence, pace and deliver curriculum material is of itself a fairly specific and somewhat limiting perspective on teaching. A perspective that I’m not sure fits real well with the approach I’m thinking of for my design. I also have a problem with the teleological nature of the design

This process enables learning managers to focus their work on learner progress and future learning objectives; to assemble the required ‘ingredients’ for a successful learning program (design) and to then implement the plan using appropriate pedagogical strategies.

But let’s leave the questioning aside and tick the boxes.

The first three LM questions have to do with profiling the student, they are

  1. What does the learner already know?
  2. Where does the learner need/want to be?
  3. How does the learner best learn?

These are the questions I’m going to seek to answer in the following. The assignment suggests/requires that we do this profiling within the context of our existing school. I’m going to argue against that because what I’m designing is not going to be taught to these students, mainly because it is quite a radical departure from existing practice. But also because there is a tendency for IPT courses to be slightly idiosyncratic in their choice of tools and approaches. For example, my IPT mentor teacher has experience and knowledge with Visual Basic and Access. So his IPT curriculum is based around the use of those tools to achieve appropriate learning outcomes. I have little to no experience with those tools.

What does the learner already know?

As a senior course, students entering the course will have completed schooling up to Year 10. This may or may not have included formal study of Information Technology (IT). It is likely, however, that enrolling students will have had some formal experience of computing in the lower grades. It is also increasingly expected that enrolling students will have access to a computer through the National Secondary School Computer Fund’s (DET, 2010) aim of a 1:1 computer to student ratio by 31 December 2011. This access to computers should increase students’ formal experience of computing to support learning.

It is also expected that a majority of students will have some level of informal experience with the use of Information and Communications Technologies (ICTs) in the form of either computers or mobile devices. The following table summarises some data from a talk on American teens (12-17) from the Pew Research Centre. The talk uses data from a survey of 800 teenagers from September 2009. Based on observations in local schools, these numbers seem, if anything, a bit low in the Australian context.

Statistics Percentage
Online 93%
No computer 8%
Own a cell phone 75%
Online with cell phone 21%
Own a game console 80%
Own a portable gaming device 51%
On a social networking service 73%
Blog 14%

It is widely suggested that people born since around 1980 – having been immersed in the use of ICTs for most of their lives – are somehow different in terms of skills and interests and that this has significant implications for education. Bennet et al (2008, p. 776) suggest that these poorly evidenced claims have created a a “moral panic” that has restricted critical and rational debate. Jones, Ramanau et al (2010, p 722) through their examination of first year undergraduates at five English Universities that

the generation is not homogenous in its use and appreciation of new technologies and that there are significant variations amongst students that lie within the Net generation age band

Hargittai (2010) found that there is significant variation in Internet know-how amongst young adults and that those from more privileged backgrounds use the Internet in a larger number of activities and in more informed ways.

The majority will not be familiar with the requirements and practices of a computing professional. In particular, the constraints and interesting effects that having real users can generate. In particular, the gulf that can be created between an IT team and its users. Students, like most people, tend to assume that IT is either programming or desktop support. Computer science – a more theoretical ancestor of IT – has long tried to dissuade people of the narrow and misleading image of computer science as programming (Fletcher and Lu, 2009).

It is also likely that the students enrolling in IPT will not be representative of the full diversity of senior students. Bias and stereotyping of computing and the people who do computing continues to limit the diversity of people actively studying and becoming computing professionals (Klawe, Whitney and Simard, 2009). Some have concluded that men have a relative advantage over women when learning about or using computers (Cooper, 2006). Kaarst-Brown and Guzman (2010), however, argue that this focus on the characteristics of gender-based, or other, groupings is insufficient to explain individual attraction to a Science Technology Engineering and Mathematics (STEM) career. Instead, they argue that a new cultural perspective – one of which they provide – is necessary to generate renewed thinking about attracting students to IT studies (Kaarst-Brown and Guzman, 2010, p 63).

Where does the learner need/want to be?

This is where I’ll need to draw on some of the information from the last post which briefly examined the IPT syllabus for Queensland schools.

In terms of a hidden curriculum some of the following are potential candidates

  • An understanding of how creative working with IT can be.
  • A sense that they can produce something useful and make a contribution beyond school and family.

How does the learner best learn?

For some there is an expectation that the question of learning styles will in some way be included as a response to this question. Pashler, McDaniel et al (2008) argue that while there is evidence of learning preferences there is little research that suggests a positive connection between learning outcomes and differentiation of learning based on those preferences. There is, however, evidence that teaching and learning strategies that support the learning style preferences students can increase the motivation of students to learn (Feldgen and Clue, 2004). Platsidou and Metallidou (2009) offer another perspective, suggesting that learning styles inventories are more useful as a tool to encourage self-development of individual students, rather than as a mechanism to categorise and group students. The preceding mixed messages along with the difficulty involved in effectively pre-designing teaching and learning strategies based on assumptions around the mix of potential learning styles of students limit the attraction of this approach. It does appear more effective to make students aware of their learning preferences, the existence of other learning styles, adopt a course design that allows students to adopt and adapt their own learning strategies, and embed into that design approaches that encourage students to reflect and modify their strategies.

The book “How People Learn” (Committee on Developments in the Science of Learning, 2000, pp 14-19) presents three key findings related to learning that have a good research based and implications for teaching. These three findings are:

  1. Students come to the classroom with preconceptions about how the world works. If their initial understanding is not engaged, they may fail to grasp the new concepts and information that are taught, or they may learn them for purposes of a test but revert to their preconceptions outside the classroom.
  2. To develop competence in an area of inquiry, students must: (a) have a deep foundation of factual knowledge, (b) understand facts and ideas in the context of a conceptual framework, and (c) organize knowledge in ways that facilitate retrieval and application.
  3. A “metacognitive” approach to instruction can help students learn to take control of their own learning by defining learning goals and monitoring their progress in achieving them.

As mentioned in previous work for this course

Too many IT courses rely on simple and narrow problems in order to focus on the principles. The readings on constructivism, connectivism, and Engagement Theory (Kearsley & Shneiderman, 1998) have reinforced the learning and motivational advantages of engaging students in authentic problems.

Within the literature on teaching IT, computer science, and related fields there has been a range of work that has arisen from this type of observation. Maloney et al (2008) and McDougall and Boyle (2004) report on approaches where with appropriate scaffolding students are helped to learn via bricolage with much of the learning initiated by the student and help arising mostly from peers and mentors, rather than the teacher. The computer clubhouse model (Kafai et al, 2009), an after-school learning environment, is based on four core principles: support learning through design experiences, help youth build on their own interests, cultivate an “emergent community”, and create and environment of respect and trust. Tagney et al (2010) build and extend on the clubhouse model with a team-based system with teams adopting a project-oriented approach working to meet set objectives with hard deadlines. The work of Tagney et al (2010), and some of the other work described here, is based on one or both of Vygotsky’s version of social constructivism and Papert’s constructionism (1993). I see some significant value from a connectivist perspective of encouraging students to interact with communities outside the classroom, especially existing communities associated with real life systems.

The research literature on encouraging greater diversity amongst IT related disciplines (e.g. Kaarst-Brown and Guzman, 2010; Cooper, 2006; Klawe et al, 2009) suggest a range of strategies that can aid non-traditional learners. A small sample of these include:

  • Allow female students to use computers in same sex groups or alone (Cooper, 2006).
  • Work with female students on how the attribute success and failure (Cooper, 2006).
  • Incorporate opportunities to see non-traditional role models (Cooper, 2006).
  • Engage students actively in their conceptions of the IT culture and demonstrate alternatives (Kaarst-Brown and Guzman, 2010).

Some conclusions from the profile

The following is a short list of arbitrary thoughts that arose while writing the above.

  • The class design should aim to dissuade students that IT is programming or desktop support.
    Don’t start with programming. Though Fletcher and Lu (2009) disagree, so more thought here. Give students experience at the full range of computing roles: from level 1 helpdesk support through to management.
  • Computational thinking is the new term to describe what some see as the predecessor to programming.
    This may also cover some of what the IT syllabus talks about. From Fletcher and Lu (2009)

    The redesign and implementation of K–12 curricula to provide adequate exposure to and practice in CT should, of course, be coupled with ongoing efforts to rethink the ways in which we transition students into programming and higher-level CS.


Bennett, S., Maton, K., & Kervin, L. (2008). The Òdigital nativesÓ debate: A critical review of the evidence. British Journal of Educational Technology, 39(5), 775-786. doi: 10.1111/j.1467-8535.2007.00793.x.

Committee on Developments in the Science of Learning. (2000). How people learn: Brain, mind, experience and school. Washington DC: National Academy Press.

Cooper, J. (2006). The digital divide: The special case of gender. Journal of Computer Assisted Learning, 22(5), 320–334. Wiley Online Library. doi: 10.1111/j.1365-2729.2006.00185.x.

DET. (2010). National Secondary School Computer Fund Queensland State Schools Guidelines Contents. Brisbane, Queensland, Australia. Retrieved from http://education.qld.gov.au/smartclassrooms/pdf/nsscf-guidelines.pdf.

Feldgen, M., & Clua, O. (2004). Games as a motivation for freshman students to learn programming. Frontiers in Education (Vol. 3, p. S1H/11-S1H/16). Savannah, GA: IEEE.

Fletcher, G. H. L., & Lu, J. J. (2009). Human computing skills: Rethinking the K-12 experience. Communications of the ACM, 52(2), 23. doi: 10.1145/1461928.1461938.

Hargittai, E. (2010). Digital Na(t)ives? Variation in Internet Skills and Uses among Members of the ÒNet Generation.Ó Sociological Inquiry, 80(1), 92-113. doi: 10.1111/j.1475-682X.2009.00317.x.

Jones, C., Ramanau, R., Cross, S., & Healing, G. (2010). Net generation or Digital Natives: Is there a distinct new generation entering university? Computers & Education, 54(3), 722-732. Elsevier Ltd. doi: 10.1016/j.compedu.2009.09.022.

Kaarst-Brown, M. L., & Guzman, I. R. (2010). A cultural perspective on individual choices of STEM education and subsequent occupations. Proceedings of the 2010 Special Interest Group on Management Information Systemʼs 48th annual conference on Computer personnel research on Computer personnel research – SIGMIS-CPR Õ10 (p. 55). New York, New York, USA: ACM Press. doi: 10.1145/1796900.1796926.

Kafai, Yasmin, Peppler, K., & Chapman, R. (2009). The Computer Clubhouse: Constructionism and creativity in youth communities (p. 162). New York: Teachers College Press.

Klawe, M., Whitney, T., & Simard, C. (2009). Women in computing—take 2. Communications of the ACM, 52(2), 68–76. ACM. Retrieved June 1, 2011, from http://portal.acm.org/citation.cfm?id=1461947.

Maloney, J. H., Peppler, K., Kafai, Y., Resnick, M., & Rusk, N. (2008). Programming by choice: urban youth learning programming with scratch. ACM SIGCSE Bulletin, 40(1), 367–371. ACM. Retrieved June 1, 2011, from http://portal.acm.org/citation.cfm?id=1352322.1352260.

McDougall, A., & Boyle, M. (2004). Student Strategies for Learning Computer Programming: Implications for Pedagogy in Informatics. Education and Information Technologies, 9(2), 109–116. Springer. Retrieved June 1, 2011, from http://www.springerlink.com/index/H221286T0727KQ50.pdf.

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Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. Wiley-Blackwell. Retrieved May 29, 2011, from http://www.ingentaconnect.com/content/bpl/pspi/2008/00000009/00000003/art00002.

Platsidou, M., & Metallidou, P. (2009). Validity and Reliability Issues of Two Learning Style Inventories in a Greek Sample : Kolb Õ s Learning Style Inventory and Felder & Soloman Õ s Index of Learning Styles. International Journal of Teaching and Learning in Higher Education, 20(3), 324-335.

Tangney, B., Oldham, E., Conneely, C., Barrett, S., & Lawlor, J. (2010). Pedagogy and Processes for a Computer Programming Outreach Workshop—The Bridge to College Model. Education, IEEE Transactions on, 53(1), 53–60. IEEE. doi: 10.1109/TE.2009.2023210.

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