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Episode 2.

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Leaders in all types of organizations,

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education included, are concerned with performance.

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They want to measure the progress that they're making towards the strategic goals they've defined for the organization.

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They want to measure the degree to which individuals and groups are contributing to the logistic effectiveness and inefficiency.

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They want to know if the resources they dedicate to systems and initiatives are worth the cost.

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The nature of the data leaders use varies and business data are usually monetary,

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but increasingly other measures are being used as well. In education, the data are almost always test scores.

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The appropriateness of those tests as a measure for what really matters in education is actively debated,

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but we're not going to debate that here.

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The examples I've mentioned so far are of course quantitative. They deal with numbers, but many researchers also value qualitative data.

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What people say when they're describing their experiences is valuable information for leaders to know as well.

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While the IT folks in schools may have their opinions,

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it's really not up to them to decide what data to collect, how to analyze it, or how to report it.

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They do help manage databases and generate reports and perhaps give advice on how to effectively present data,

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but educators decide the data to use and its interpretation.

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So IT leaders in schools do have a responsibility to support their leaders' use of data,

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but they also have a responsibility to use data to make their own plans and decisions,

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especially with regards to system design.

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We want to document and understand the degree to which our systems are helping people to do their jobs.

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Fortunately for IT leaders in schools, there's a long tradition of using data to support those decisions,

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and there's a rich research tradition that has elucidated models to help us understand what really matters when we're designing IT for users.

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Of course, the default option for data collection in schools and most other organizations

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is to ask folks to fill out a survey. The quality of that data is always suspect.

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We ask questions that we think matter, but they might not.

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We're careful to avoid bias in the questions that we ask, but we can never catch it all.

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We usually ask everyone to answer the survey, but response rates are poor and those who do respond typically represent a special case.

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Either they really like what we're doing and want to tell us about it,

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or they really dislike what we're doing and they want to tell us that as well.

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By adopting and perhaps adapting the instruments used by technology researchers,

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we as IT leaders do enjoy several benefits.

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First, the instruments have been tested for validity and reliability.

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They really do measure what they claim to measure, and higher scores on these instruments really do correlate with better systems.

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Second, the instruments have a theoretical basis.

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If scores are low on these measures, then we can usually gain insight into what we can do to improve the performance of our systems.

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Because we can recognize and understand the theory behind the measures,

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we're more likely to actually improve performance rather than simply improve scores on the test.

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And that is not something that we can do if we're using a survey that we've made ourselves.

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In my experience, the technology acceptance model is a theoretical model that translates into easy data collection

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and that can identify aspects of our IT systems that must be improved.

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The technology acceptance model was first described in 1989,

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and it has been refined and updated several times in the intervening years,

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and it has been adapted for various populations and purposes.

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I typically use the unified theory of acceptance and use of technology,

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which was described in 2003 when I want to gather data about the IT that I manage.

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According to the UTAUT, people tend to use technology when four things are true.

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First, if they perceive it to be easy to use.

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Second, if they perceive it to be effective.

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Third, if others whom they respect use it or similar systems.

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And fourth, if it's well supported.

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Now, of course, I'm summarizing what's really an extensive body of research.

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And my language is a little different from that which is used to define it,

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but the ideas are the same.

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If you're interested in the original research, head on over to my website at hackscience.education.

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Follow the link to my blog in search for UTAUT.

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Several posts have links to the papers in which this work was originally elucidated.

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One common element of all variations on the technology acceptance model

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is a focus on users' perceptions.

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If you as an IT leader or technician believe that the systems are easy to use,

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but your users do not, then it is not easy to use.

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This can be hard for many IT folks to hear, but that perception is reality.

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Now, technology acceptance is also based on the assumption

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that these factors will positively affect people's intention to use technology,

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which then affects their actual use of technology.

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In many school settings, the use of IT systems is not optional.

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The teachers use a grade book and the student information system that they've been directed to use,

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and they don't have an option to not use it.

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Interestingly, technology acceptance can influence design decisions for those compulsory systems as well.

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If we customize a grade book, for example, so that it aligns with the factors associated with technology acceptance,

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then teachers are likely to dig a little deeper into software

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so that it more efficiently and effectively captures and communicates student progress.

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In a later episode, I'll dig a little deeper into the unified theory of acceptance and use of technology

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and perceived effort expectancy, performance expectancy, social influences, and facilitating conditions.

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Those are the terms that were used when the theory was originally defined.

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I'll do that in a later episode, however.

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In this episode, I hope you understand that IT leaders in schools, as well as in other organizations,

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should design systems to align with ease of use, effectiveness, social influences, and facilitating conditions.

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You can save the time of writing surveys by using instruments that have already been developed to measure technology acceptance.

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I haven't mentioned this yet, but you can also adopt researchers' approaches to sampling

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by choosing a small random sample and get data that's more representative of the whole population

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than just asking everyone to respond and having those people who are most interested responding.

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You can also integrate technology acceptance into your conversations with users and your interpretations of their complaints.

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Ask what changes could be made to make the system easier.

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Listen to the changes that they say they need in order to do their job.

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Listen to what they say about others who do the same work that they do and the systems that they have.

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One last thing to remember about technology acceptance for this episode is that communication has important influences on perceptions.

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Of course, ineffective communication is a problem for all departments and in all organizations,

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but school IT leaders who take the steps to tell others what they've been doing

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and also who listen to what users have to say are much more likely to have systems that are used.

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And isn't that why we do the work that we do?