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The increasing popularity of e-learning has created a need for accurate student achievement prediction mechanisms, allowing instructors to improve the efficiency of their courses by addressing specific needs of their students at an early stage. In this paper, a student achievement prediction method applied to a 10-week introductory level e-learning course is presented. The proposed method uses multiple feed-forward neural networks to dynamically predict students' final achievement and to cluster them in two virtual groups, according to their performance. Multiple-choice test grades were used as the input data set of the networks.This form of test was preferred for its objectivity. Results showed that accurate prediction is possible at an early stage, more specifically at the third week of the 10-week course. In addition, when students were clustered, low misplacement rates demonstrated the adequacy of the approach. The results of the proposed method were compared against those of linear regression and the neural-network approach was found to be more effective in all prediction stages. The proposed methodology is expected to support instructors in providing better educational services as well as customized assistance according to students' predicted level of performance.

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Forming work teams involves matching people with complementary skills and personalities, but requires obtaining such data a priori. We introduce team dating, where people interact on brief tasks before working with a dedicated partner for longer, more complex tasks. We studied team dating through two online experiments. In Experiment 1, workers from a crowd platform independently wrote an ad slogan, discussed it with three consecutive people and evaluated their team date interactions. They then selected preferred teammates from a list showing average ratings for people they had dated and not dated. Results show that participants evaluated their dates based on evidence beyond externally judged slogan quality, and relied heavily on their dyad-specific judgments in selecting teammates. In Experiment 2, we replicated the individual and team dating tasks, and formed teams, either i) by honoring pairwise team dating preferences, ii) randomly from their pool of dates, or iii) randomly from those not dated. Results show that teams formed from preferred dates performed better on a final creative task compared to random dates or non-dates. Team dating provides a dynamic technique for forming ad hoc teams accounting for interpersonal dynamics. The initial interactions provided information that helped people select and work with an appropriate teammate.