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Students’ intentions to use technology in their learning: The effects of internal and external conditions
- 1. Students’ intentions to use technology in their learning: The effects of internal and external conditions Alexander Whitelock-Wainwright School of Psychology, University of Liverpool A.Wainwright@Liverpool.ac.uk
- 2. Research Aims • Individual differences in learning tool usage. • Theory driven approach to understand intentions to use learning analytics services.
- 3. Conditions of Learning • External and internal conditions (Butler & Winne, 1995). • Patterns of tool usage influenced by learner conditions (Lust, Collazo, Elen, & Clarebout, 2012).
- 4. Beliefs • Usage of learning analytics services will vary across students. • Beliefs are shaped by exposure to various sources of information (Fishbein, 1967).
- 5. Expectations • Pre-trial beliefs correspond to expectations (Olson & Dover, 1979). • Exposure/use of a service will disconfirm these expectations (Oliver, 1980).
- 6. Disconfirmation Expectations Service Exposure Perceptions Attitude Intentions to Continue Using Service Fishbein (1967); Oliver (1980)
- 7. Current Issues • Need to accommodate student expectations in learning analytics policy development. • Assurance of service quality. • Need an instrument to explore expectations.
- 8. Questionnaire Development • Identifying themes within past literature (Ifenthaler & Schumacher, 2016; Sclater, 2016; West, Heath, & Huijser, 2016): • Ethics and Privacy • Meaningfulness • Agency • Interventions
- 9. Questionnaire Development (Contd.) Expectations Predictive Desired Swan & Trawick (1980)
- 10. Questionnaire Development (Contd.) Created 79 Items Peer Review Pilot Study with 37 Items Exploratory Factor Analysis Redistribute the Questionnaire
- 11. Pilot Study Results • Instrument reduced to 19 items. • Two factor solution for both scales: • Service expectations: o Desires scale – 0.88 alpha. o Predictive scale – 0.88 alpha. • Ethical expectations: o Desires scale – 0.82 alpha. o Predictive scale – 0.86 alpha.
- 12. Pilot Study Results (Contd.)
- 13. Pilot Study Results (Contd.)
- 14. Pilot Study Results (Contd.)
- 15. Pilot Study Results (Contd.)
- 16. Pilot Study Results (Contd.)
- 17. Future Directions • Develop the corresponding perceptions scale. • Model intentions towards using learning analytics. Attitudes Social Norms Intentions to Use Learning Analytics Perceived Behavioural Control
Editor's Notes
- The main aim of my research is to investigate individual differences in student tool use during their learning process… Particular focus has been on exploring those factors that may affect students’ intentions of using learning analytics services… which will form the main part of this talk…
- From previous work in metacognition and self-regulated learning… it has been outlined that the operations students perform whilst learning are affected by both internal and external conditions… Internal conditions relate to metacognitive knowledge… intrinsic motivation… self-efficacy… or beliefs… Whereas… external conditions are concerned with the design of the learning environment… The theoretical model put forward by Lust… shows that together these can be thought of as learner conditions… which affect the way in which we engage with certain technologies… So… a student who has may have a higher intrinsic motivation may show patterns of behaviour in using a learning tool that is distinctly different to a student with a higher extrinsic motivation…
- With these various factors in mind… we considered how our beliefs may be an important determinant in affecting student intentions to use a learning analytics services… The reason for conceptualising learning analytics as a service is that with the eventual roll out of analytics across universities… it will become an additional tool that is aimed at supporting or improving student learning… Even though… from a research… or managerial position… learning analytics will be considered as beneficial for student learning… students may not feel the same way… and one of these reasons may relate to the service not meeting initial expectations This leads into my current research… which is exploring how the disconfirmation of student expectations subsequently affects intentions to use learning analytics services… If we consider Fishbein’s work on attitudes… he discusses how we hold various beliefs about objects and their attributes… And these beliefs are formed through the exposure of various sources of information… With each of these beliefs… we also evaluate whether the attribute is good or bad… and together this forms our overall attitude… In other words… if a learning analytics system provides visualisations of my learning progress… and I see that as good… then my attitude towards using the learning analytics service will be positive…
- For majority of students… however… they have yet to be exposed to learning analytics services… so we cannot examine beliefs in this way… as they would not have any opinion on whether these features are good or not… So instead we sought to explore student expectations towards learning analytics services… Expectations can be thought of as our pre-trial beliefs… in other what we expect from a service before using it… Again these expectations are shaped through our exposure to various sources of information… such as the university or instructors… According to the expectation disconfirmation theory… when we become exposed to a service we disconfirm our initial expectations based on whether the reality of the service meets or fails to align with our initial expectations…
- To exemplify the effects of disconfirming pre-trial beliefs… here is a simplistic integration of the theory of reason action and the expectation-disconfirmation model… As previously stated… before any exposure to a service… we hold expectations about what we believe the service should offer… so a student may hold expectations about receiving accurate and timely information about their learning progress… This expectation may have formed through the information a university provides to them… So when the student becomes exposed… or make use out of this service… they begin to disconfirm these expectations… this means that they are seeing the service as exceeding, aligning, or falling short with their initial beliefs… This will then affect an individuals attitude towards using the system… For example… if the student found the service to meet or exceed what they initially expected… then their intentions to continue using the system will be higher as their attitude towards using it will be positive On the other hand… failing to meet these expectations will lead them to think using the learning analytics service is a bad thing… so their intentions to use this service in the future will be lower…
- There are a lot of stakeholders involved in learning analytics… all of whom will hold different expectations about what they want from such a service… so there is a risk that a future service could satisfy one group over and above others… A consequence of this could be an increase in negative attitudes… which could affect intentions towards using learning analytics services… So the application of the expectation disconfirmation approach will be beneficial for both learning analytics policy development… and the assurance of service quality… as it can be used to assess whether the university is meeting the expectations of students… In order for this to be achieved… however… the learning analytics community needs an instrument that can explore expectations of students towards learning analytics… which is what I am currently developing…
- In order to create the survey items… I conducted a literature review of learning analytics papers concerning ethics and privacy issues… Within these papers… a number of themes regularly came up… which can be grouped into the following types of expectations Ethics and privacy... This can relate to the idea of whether students expect their data to kept securely… and remain confidential… Meaningfulness… this is mainly concerned with the feedback from learning analytics being provided in a format that is accessible… and understandable… Agency… this relates to learning analytics being student-centred… so will students have control over whether they can make sense out of their own data… or will the institution do this for them And… interventions… which is concerned with what students expect results of learning analytics to be used for… will an intervention be aimed at improving academic skills like writing… or will there be more focus on emotional support
- A further issue relates to the term expectation being ambiguous… it cannot be thought simply as what an individual expects… Instead… expectations can be thought of as either… predictive… which an individual’s pre-trial belief of how what a service will achieve… Or… desired… which is the pre-trial belief of what level of service performance would be necessary to please the individual By separating expectations out like this… a more detailed understanding of satisfaction can emerge… So… if the perceptions of a service meet… or exceeded my desired expectations… then I will be satisfied with the service Meeting the predictive expectations… on the other hand… will lead to a feeling of indifference… Failure to meet either expectation… will be a cause of dissatisfaction
- Following the literature review… I created 79 items… which were then subject to peer review… The peer review allowed me to reduce the number of items… as the feedback highlighted items that were quite similar… We were then left with 37 items for a pilot study… each item contains two subscales… one for predictive expectations… and one for desired expectations… 210 respondents took part in the pilot study… where they completed the questionnaire and provided qualitative comments about the clarity of each question… An exploratory factor analysis was ran on the quantitative data collected from the questionnaire… which allowed for the identification of underlying latent variables… The qualitative comments were also used to determine whether items needed changing or removing…
- The factor analysis showed a two factor solution to be sufficient for both scales… with the same items loading on these factors across both scales These factors can be viewed as service and ethical expectations… For the service expectations… desires and predictive subscales had 0.88 reliability For ethical expectations… the desires scale had a 0.82 reliability and the predictive scale had a 0.86 reliability…
- The next five slides will present a brief overview of the pilot data… Firstly… the item with the lowest average response related to whether teachers were obligated to act if a student was found to be underperforming.
- This plot shows the highest average response for the predictive expectations scale… this was for the item relating to the university ensuring that all collected data will be kept securely…
- The highest average response item for the desires expectation scale was for the item concerning students being asked for consent before data was sent to third party companies…
- The smallest difference between both subscales was for the item relating to using identifiable data… so from this it can be inferred that students have strong opinions towards the university asking for any consent for identifiable data usage…
- Whereas… the greatest difference between subscales was for the item concerning the feedback being easy to understand…
- The next steps for this research project… are to first… re-distribute the questionnaire to a larger sample of students… whose responses will be subject to a further exploratory factor analysis… Then a final distribution of the questionnaire will be undertaken… and a confirmatory factor analysis ran to assess the validity of the scale. Once the expectation scale has been developed… there is a need for the corresponding perception scale… once this has been achieved we can then explore whether service expectations are being met… The complete instrument for measuring expectations and perceptions can be an important step in introducing theory into learning analytics… as it can placed within the theory of planned behaviour framework as to model the reasons why students may or may not use the learning analytics systems in place… This is important to consider… as the provision of a tool designed to support learning may be beneficial… from a managerial point of view… but if the student holds a negative attitude towards using it… then they may have no intention to use it at all… So if students have no intention of using a learning analytics service or tool… then how will it be beneficial to learning?