In previous articles, we discussed the basic concepts of gamification and game-based assessments. An important point to recall is that gamification can be seen as lying on a continuum and is therefore not just an on/off application of gaming principles. Instead, assessments vary in the degree to which they apply gaming concepts, from surface-level to more integrated, game-centric applications.

With the increasing growth and expansion of assessment technologies come unique questions in how best practice can be maintained in psychometric testing. A key issue around technology and online assessments relates to the impact that various technologies have on accessibility, standardisation, fairness and consistency.

In South Africa, we face challenges of unemployment, poverty, and a growing number of young people who are not receiving the educational opportunities they need. Fortunately, several organisations have stepped to the fore to make a difference and reverse the trend.

When faced with unique or difficult recruitment and selection challenges, organisations are sometimes reluctant to use traditional assessments to filter out unsuitable candidates. This is, in part, because of the perceived cost and complexity of psychometric measures, but also because recruiting managers fear that traditional testing is too abstract to accurately communicate the company’s brand and values to prospective employees.

In previous articles, we examined the potential benefits of mobile assessments for the IO Profession. In addition, we’ve started a recent series of articles on the advantages of Situational Judgement Tests (SJTs). What if we combine both? In today’s article, we look at just such an example: cut-e’s exciting chatAssess technology.

Introduction: Why situation matters

IO Practitioners who want to make better talent decisions often use measures of ability and personality to inform their hiring practices. As we’ve discussed in previous posts, this is a very good idea indeed. Using objective, scientifically-validated assessments avoids subjective, biased decision-making and predicts job performance far beyond chance levels. However, it would be remiss not to also consider the situation within which work behavior occurs. Put another way, it is sometimes important to know how people would behave in very specific circumstances, rather than knowing their general disposition across situations.

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