Aim
This pilot study aimed to explore the impact of Smart Home technology to support older people’s quality of life, particularly for those who live alone.
Background
There has been an increased interest in using innovative technologies and artificial intelligence to enable Smart Home technology to support older people to age independently in their own homes.
Methods
This study used a pre‐and post‐test design. The seven item Personal Wellbeing Index was used to measure participants’ subjective quality of life across seven quality of life domains. Participants (n = 60) aged between 68 and 90 years (M = 80.10, SD = 5.56) completed a 12‐week personalised Smart Home technology program.
Results
Approximately half of the participants lived alone (48.3%). Participants’ quality of life significantly increased (p = 0.010) after Smart Home use. Two domains, “achieving in life” (p = 0.026) and “future security” (p = 0.004), were also significantly improved after participating in the Smart Home technology program. Improvements in quality of life did not vary as a function of living arrangement (all ps > .152, all ηp2 > .00).
Conclusion
The current study provides preliminary evidence for the role of Smart Home technology in supporting older people’s quality of life, particularly their sense of achieving in life and future security.
This paper describes a design-based implementation research (DBIR) approach to the development and trialling of a new generation massive open online course (ngMOOC) situated in an instructional setting of undergraduate mathematics at a regional Australian university. This process is underscored by two important innovations: (a) a basis in a well-established human cognitive architecture in terms of cognitive load theory; and (b) point-of-contact feedback based in a well-tested online system dedicated to enhancing the learning process. Analysis of preliminary trials suggests that the DBIR approach to the ngMOOC construction and development supports theoretical standpoints that argue for an understanding of how design for optimal learning can utilise conditions, such as differing online or blended educational contexts, in order to be effective and scalable. The ngMOOC development described in this paper marks the adoption of a cognitive architecture in conjunction with feedback systems, offering the groundwork for use of adaptive systems that cater for learner expertise. This approach seems especially useful in constructing and developing online learning that is self-paced and curriculum-based.
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