Achieving Game Goals at All Costs? : The Effect of Reward Structures on Tactics Employed in Educational Military Wargaming

A key motive in using gaming for educational purposes is to enhance user motivation and involvement to the subject matter. Within military education, games have always been utilized as a means to think clearly about military operations. However, some research results have shown that gaming, regardless of what the game is supposed to portray, is a meaningful activity in itself, and this can distract the learner away from the educational objective. Playing the game, then, becomes similar to competition, such as in sports where the objective is to only win the game. The player directs actions to achieving game goals even though some actions are inappropriate from a learning perspective. To shed light on the discrepancy between playing a game to win and playing a game to learn, we conducted an experiment on cadets playing an educational wargame. By varying the conditions of the game, playing with or without points, while still in line with the learning objective, we were interested to see what impact it had on the tactics employed by cadets. The results showed that adding reward structures, such as points, changed the outcome of the game, that is, groups playing with points played the game more aggressively and utilized the military units more extensively. These findings suggest that changes in the game design, although educationally relevant, may distract learners to be more oriented towards a lusory attitude, in which achieving the game goals becomes players' biggest concern

Business games and simulations: Which factors play key roles in learning

The paper reports the results of an empirical study on the effects and impact of a specific business game, which is also a team competition, treated as an innovative teaching tool in learning. The paper starts by introducing business games and simulations as methods able to improve learning experiences and goes on by dealing with the specific business game simulation used for the aims of our research. Considering the most relevant empirical studies on business games and simulations, the following four factors were extracted in order to test their importance for learning: Decision-Making Experience (DME), Teamwork (T), Simulation Experience Satisfaction (SES), Learning Aims (LA). Each construct has been investigated by using a questionnaire administrated to 48 participants of the Stock Market Learning Simulation divided into 10 teams. Results show the importance of these factors in detecting critics aspectal of learning using a business game simulation

1000 Bull Genomes Consortium Project

Genomic selection, where selection decisions are based on estimates of breeding value from genome wide-marker effects, has enormous potential to improve genetic gain in dairy and beef cattle. Although successful in dairy cattle, some major challenges remain 1) only a proportion of the genetic variance is captured, particularly for some traits 2) marker effects are rarely consistent across breeds, 3) accuracy of genomic predictions decays rapidly over time. Using full genome sequences rather than DNA markers in genomic selection could address these challenges. However, sequencing all individuals in the very large resource populations required to estimate the typically small effects of mutations on target traits would be prohibitively expensive. An alternative is to sequence key ancestors contributing most of the genetic material of the current population, and to use this reference for imputation of sequence from SNP chip data. The reference set must still be large, in order to capture for example, rare variants which are likely to explain some of the variation in our target traits. Recognising the need for a comprehensive “reference set” of key ancestors by many groups undertaking cattle research and cattle breeding programs, we have initiated the 1000 bull genomes project. The project will assemble whole genome sequences of cattle from institutions around the world, to provide an extended data base for imputation of genetic variants. This will enable the bovine genomics community to impute full genome sequence from SNP genotypes, and then use this data for genomic selection, and rapid discovery of causal mutations. Some preliminary results from the variant detection pipeline will be reported

1000 Bull Genomes - Toward genomic Selectionf from whole genome sequence Data in Dairy and Beef Cattle

Genomic prediction of breeding values is now used as the basis for selection of dairy cattle, and in some cases beef cattle, in a number of countries. When genomic prediction was introduced most of the information was to thought to be derived from linkage disequilibrium between markers and causative variants. It has become clear that much of the predictive power, based on 50,000 DNA markers, in fact derives from prediction of the effect of large chromosome segments that segregate within fairly closely related animals. This has lead to problems with across breed prediction, rapid decay of predictive power over generations and insufficient accuracy in some situations. Using full genome sequence data in genomic prediction should overcome these problems. If linkage disequilibrium between SNP on standard arrays and causative mutations affecting the quantitative trait is incomplete, accuracy of prediction should be improved as a result of including the actual causative mutations affecting the trait of interest in the data set. Secondly, persistence of accuracy of genomic predictions across generations will be improved with full sequence data, as the genomic predictions no longer depend on associations between SNP and causative mutations which currently erode quite rapidly with recombination. Thirdly, if genomic predictions are made across breeds, using full sequence data is likely to be particularly advantageous, as there is no longer a need to rely on marker- associations which may not persist across breeds. However, the cost of sequencing is such that the very large numbers of animals required for genomic prediction will not be sequenced An alternative strategy is to sequence key ancestors of the population, then impute the genotypes for the sequence variants into much larger reference sets with phenotypes and SNP panel genotypes. The 1000 Bull Genomes Project aims at building such a resource of sequenced key ancestor bulls for the bovine research community. The most recent run of the project included 238 full genome sequences of 130 Holstein, 43 Fleckvieh, 48 Angus and 15 Jersey bulls, sequenced at an average of 10.5 fold coverage. There were 25.2 million filtered sequence variants detected in the sequences, including 23.5 million SNP and 1.7 million insertion-deletions. Agreement of sequence genotypes to genotypes from an 800K SNP array in the sequenced Holstein bulls, where there was most data, was excellent at 98.8%. This increased to 99.7% when the genotypes were imputed based on all sequences. Concordance was slightly lower in other breeds. This project will provide an excellent opportunity to identify the most important causative variants, leading to greater understanding of biology underlying quantitative traits. Examples are given of genomic predictions for fertility, health and production traits using imputed sequence data

1000 Bull Genomes Consortium Project

Genomic selection, where selection decisions are based on estimates of breeding value from genome wide-marker effects, has enormous potential to improve genetic gain in dairy and beef cattle. Although successful in dairy cattle, some major challenges remain 1) only a proportion of the genetic variance is captured, particularly for some traits 2) marker effects are rarely consistent across breeds, 3) accuracy of genomic predictions decays rapidly over time. Using full genome sequences rather than DNA markers in genomic selection could address these challenges. However, sequencing all individuals in the very large resource populations required to estimate the typically small effects of mutations on target traits would be prohibitively expensive. An alternative is to sequence key ancestors contributing most of the genetic material of the current population, and to use this reference for imputation of sequence from SNP chip data. The reference set must still be large, in order to capture for example, rare variants which are likely to explain some of the variation in our target traits. Recognising the need for a comprehensive “reference set” of key ancestors by many groups undertaking cattle research and cattle breeding programs, we have initiated the 1000 bull genomes project. The project will assemble whole genome sequences of cattle from institutions around the world, to provide an extended data base for imputation of genetic variants. This will enable the bovine genomics community to impute full genome sequence from SNP genotypes, and then use this data for genomic selection, and rapid discovery of causal mutations. Some preliminary results from the variant detection pipeline will be reported