{'fraction_non_alphanumeric': 0.02035048049745619, 'fraction_numerical': 0.0, 'mean_word_length': 5.531365313653136, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Machine Learning-Based External Assessment Engine for GIFT to Support Team Training in Dismounted Battle Drill Operations

In urban combat scenarios performed by the armed forces, effective teamwork is essential to mission success. Soldiers must be able to rapidly make decisions regarding their own and their team behavior and effectively communicate and synchronize those decisions with their squad members. Developing proficiency in such team-oriented tasks requires repeated practice and training exercises, preferably accompanied by feedback and scaffolding of the team. In this work, we discuss our research in the development of intelligent training systems to support evaluation of individual and team performance during training exercises in synthetic training environments (STEs). To demonstrate the effectiveness of our research approach, we develop an external assessment engine (EAE) for the Generalized Intelligent Framework for Tutoring (GIFT). Our engine utilizes the multi-modal data generated by Soldiers’ interactions with the STE environment. Through a combination of machine learning methods (e.g., motion tracking, and posture and gesture detection) and log data generated of Soldiers’ actions and their outcomes from the STE, we compute automated performance metrics for squads of Soldiers that operate as teams in the scenario. The generated metrics span both individual and team performance across a variety of psychomotor, cognitive, and coordination skills and strategies. In this work, we discuss the high-level framework and design our EAE with application to dismounted battle drills for the armed forces, though the analysis techniques we develop are general they can be easily modified to apply to a variety of team training scenarios.