Effects of TimeSlips on Cornell Scale for Depression in Dementia scores of senile dementia patients

Objective:To investigate the influences of TimeSlips on the Cornell Scale for Depression in Dementia(CSDD)scores of mild or moderate senile dementia patients.Methods:Forty-three cases of mild or moderate senile dementia patients were selected locally for convenience sake and given the TimeSlips intervention.The patients were assessed using the scales of CSDD and the Observed Emotion Rating Scale(OERS).Results:The differences of the patients'CSDD scores between before and after the intervention were statistically significant(P<0.05).The differences of the patients'OERS scores on positive and negative emotions between before and after the intervention were also statistically significant(P<0.05).Conclusion:TimeSlips is beneficial to relieve depressive symptoms and ameliorate the emotions of mild or moderate senile dementia patients,thus improving their life quality and reducing the burden of their caregivers.A large-scale experimental research on TimeSlips with rigorous design is proposed for further studies.

Large-Scale Assessments,Personalized Learning,and Creativity:Paradoxes and Possibilities

Purpose:This article,based on an invited talk,aims to explore the relationship among large-scale assessments,creativity and personalized learning.Design/Approach/Methods:Starting with the working definition of large-scale assessments,creativity,and personalized learning,this article identified the paradox of combining these three components together.As a consequence,a logic mode of large-scale assessment and creativity expressions is illustrated,along with an exploration of new possibilities.Findings:Smarter design of large-scale assessments is needed.Firstly,we need to assess creative learning at the individual level,so complex tasks with high uncertainty should be presented to students.Secondly,additional process and experiential data while students are working on problems need to be captured.Thirdly,the human-artificial intelligence(AI)augmented scoring should be explored,developed,and refined.Originality/Value:This article addresses the drawbacks of current large-scale assessments and explores possibilities for combining assessment with creativity and personalized learning.A logic model illustrating variations necessary for creative learning and considerations and cautions for designing large-scale assessments are also provided.