Towards Supporting Multigenerational Co-creation and Social Activities: Extending Learning Analytics Platforms and Beyond

As smart technologies pervade our everyday environments, they change what people should learn to live meaningfully as valuable participants of our society. For instance, ubiquitous availability of smart devices and communication networks may have reduced the burden for people to remember factual information. At the same time, they may have increased the benefits to master the uses of new digital technologies. In the midst of such a social and technological shift, we could design novel integrated platforms that support people at all ages to learn, work, collaborate, and co-create easily. In this paper, we discuss our ideas and first steps towards building an extended learning analytics platform that elderly people and unskilled adults can use. By understanding the characteristics and needs of elderly learners and addressing critical user interface issues, we can build pervasive and inclusive learning analytics platforms that trigger contextual reminders to support people at all ages to live and learn actively regardless of age-related differences of cognitive capabilities. We discuss that resolving critical usability problems for elderly people could open up a plethora of opportunities for them to search and exploit vast amount of information to achieve various goals.

[1]  Gregory D. Abowd,et al.  CybreMinder: A Context-Aware System for Supporting Reminders , 2000, HUC.

[2]  Keith Cheverst,et al.  Developing a context-aware electronic tourist guide: some issues and experiences , 2000, CHI.

[3]  Alireza Sahami Shirazi,et al.  Large-scale assessment of mobile notifications , 2014, CHI.

[4]  Norman M. Sadeh,et al.  Curated city: capturing individual city guides through social curation , 2014, CHI.

[5]  Shin'ichi Konomi,et al.  Effective Learning Environment Design for Aging Well: A Review , 2018, HCI.

[6]  Stephanie Rosenthal,et al.  Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models , 2011, Pervasive.

[7]  E. Hutchins Cognition in the wild , 1995 .

[8]  Martin Pielot,et al.  Didn't you see my message?: predicting attentiveness to mobile instant messages , 2014, CHI.

[9]  Dan Frankowski,et al.  Because I carry my cell phone anyway: functional location-based reminder applications , 2006, CHI.

[10]  Michael Roberts,et al.  Activity-based serendipitous recommendations with the Magitti mobile leisure guide , 2008, CHI.

[11]  Shin'ichi Konomi,et al.  Evaluating Learning Style-Based Grouping Strategies in Real-World Collaborative Learning Environment , 2018, HCI.

[12]  Steve Benford,et al.  Effects of content and time of delivery on receptivity to mobile interruptions , 2010, Mobile HCI.

[13]  Chris Schmandt,et al.  Location-Aware Information Delivery with ComMotion , 2000, HUC.

[14]  Ian Warren,et al.  Push Notification Mechanisms for Pervasive Smartphone Applications , 2014, IEEE Pervasive Computing.

[15]  Tsvi Kuflik,et al.  Personalization in cultural heritage: the road travelled and the one ahead , 2011, User Modeling and User-Adapted Interaction.

[16]  Alessandra Russo,et al.  Learning to recognise disruptive smartphone notifications , 2014, MobileHCI '14.

[17]  Martin Pielot,et al.  An in-situ study of mobile phone notifications , 2014, MobileHCI '14.

[18]  Paulo Blikstein,et al.  Multimodal learning analytics , 2013, LAK '13.

[19]  Alexander van Deursen,et al.  Internet skills and the digital divide , 2011, New Media Soc..

[20]  Jorge Gonçalves,et al.  Community Reminder: Participatory contextual reminder environments for local communities , 2017, Int. J. Hum. Comput. Stud..

[21]  Gregory D. Abowd,et al.  A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications , 2001, Hum. Comput. Interact..

[22]  Joyce Ho,et al.  Using context-aware computing to reduce the perceived burden of interruptions from mobile devices , 2005, CHI.

[23]  William G. Griswold,et al.  Place-Its: A Study of Location-Based Reminders on Mobile Phones , 2005, UbiComp.

[24]  Kohei Hatano,et al.  Can Machine Learning Techniques Provide Better Learning Support for Elderly People? , 2018, HCI.

[25]  Atsushi Shimada Potential of Wearable Technology for Super-Aging Societies , 2018, HCI.

[26]  Mirco Musolesi,et al.  InterruptMe: designing intelligent prompting mechanisms for pervasive applications , 2014, UbiComp.

[27]  Christopher G. Atkeson,et al.  Predicting human interruptibility with sensors , 2005, TCHI.

[28]  Steve Benford,et al.  Investigating episodes of mobile phone activity as indicators of opportune moments to deliver notifications , 2011, Mobile HCI.

[29]  Gregory D. Abowd,et al.  Cyberguide: A mobile context‐aware tour guide , 1997, Wirel. Networks.