A model for programmatic assessment in action is proposed that optimizes assessment for learning as well as decision making on learner progress. It is based on a set of assessment principles that are interpreted from empirical research. The model specifies cycles of training, assessment and learner support activities that are completed by intermediate and final moments of evaluation on aggregated data-points. Essential is that individual data-points are maximized for their learning and feedback value, whereas high stake decisions are based on the aggregation of many data-points. Expert judgment plays an important role in the program. Fundamental is the notion of sampling and bias reduction for dealing with subjectivity. Bias reduction is sought in procedural assessment strategies that are derived from qualitative research criteria. A number of challenges and opportunities are discussed around the proposed model. One of the virtues would be to move beyond the dominating psychometric discourse around individual instruments towards a systems approach of assessment design based on empirically grounded theory.
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In social settings, people often need to reason about unobservablemental content of other people, such as their beliefs, goals, orintentions. This ability helps them to understand, to predict, and evento influence the behavior of others. People can take this ability furtherby applying it recursively. For example, they use second-order theory ofmind to reason about the way others use theory of mind, as in ‘Alicebelieves that Bob does not know about the surprise party’. However,empirical evidence so far suggests that people do not spontaneously usehigher-order theory of mind in strategic games. Previous agent-basedmodeling simulations also suggest that the ability to recursively applytheory of mind may be especially effective in competitive settings. Inthis paper, we use a combination of computational agents and Bayesianmodel selection to determine to what extent people make use of higherordertheory of mind reasoning in a particular competitive game, theMod game, which can be seen as a much larger variant of the well-knownrock-paper-scissors game.We let participants play the competitive Mod game against computationaltheory of mind agents. We find that people adapt their level oftheory of mind to that of their software opponent. Surprisingly, knowinglyplaying against second- and third-order theory of mind agents enticeshuman participants to apply up to fourth-order theory of mindthemselves, thereby improving their results in the Mod game. This phenomenoncontrasts with earlier experiments about other strategic oneshotand sequential games, in which human players only displayed lowerorders of theory of mind.
PurposeThis study aims to develop an understanding of how customers of a physical retail store valuate receiving location-based mobile phone messages when they are in proximity of the store. It proposes and tests a model relating two benefits (personalization and location congruency) and two sacrifices (privacy concern and intrusiveness) to message value perceptions and store visit attitudes.Design/methodology/approachThe study uses a vignette-based survey to collect data from a sample of 1,225 customers of a fashion retailer. The postulated research model is estimated using SmartPLS 3.0 with the consistent-PLS algorithm and further validated via a post-hoc test.FindingsThe empirical testing confirms the predictive validity and robustness of the model and reveals that location congruency and intrusiveness are the location-based message characteristics with the strongest effects on message value and store visit attitude.Originality/valueThe paper adds to the underexplored field of store entry research and extends previous location-based messaging studies by integrating personalization, location congruency, privacy concern and intrusiveness into one validated model.
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