The effects of cultural and historical information and contribution threshold on public contributions: an experimental study on the conservation of heritage houses in Penang,Malaysia

Budget constraints at the local government level towards the provision of public goods have fuelled interest in the voluntary contribution and provision point mechanisms. However, due to their public nature, these mechanisms are not without problems. The literature shows the presence of free riding and socially inefficient contribution levels. This paper experimentally tests the effect of cultural and historical information pertaining to heritage houses in Penang, Malaysia, on public contributions for their conservation. This paper considers a standard linear one-shot four-person public good game and the decision of the subjects being to contribute either to a private or to a public account. We devise 4 treatments: a Control treatment, a treatment where subjects are provided with cultural and historical information pertaining to the heritage houses, a treatment that includes a contribution threshold, and finally a treatment that combines the use of cultural and historical information with the contribution threshold. The main finding shows that 60–75 % of the subjects contributed more than what they believed others in the same group would contribute when they were provided with the pertinent cultural and historical information. Most of the subjects contributed less than their belief, while 75–77 % cooperated ‘selfishly’ in treatments without the information. These findings are in agreement with the literature, namely that contributions are higher in treatments with a contribution threshold. Moreover, the combination of a contribution threshold and cultural information could encourage more pro-social behaviours.     

Preparing students for future learning with Teachable Agents

One valuable goal of instructional technologies in K-12 education is to prepare students for future learning. Two classroom studies examined whether Teachable Agents (TA) achieves this goal. TA is an instructional technology that draws on the social metaphor of teaching a computer agent to help students learn. Students teach their agent by creating concept maps. Artificial intelligence enables TA to use the concept maps to answer questions, thereby providing interactivity, a model of thinking, and feedback. Elementary schoolchildren learning science with TA exhibited “added-value” learning that did not adversely affect the “basic-value” they gained from their regular curriculum, despite trade-offs in instructional time. Moreover, TA prepared students to learn new science content from their regular lessons, even when they were no longer using the software.