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Artificial neural networks (ANNs) consist of a family of techniques that are commonly employed to recognize and interpret patterns in big data that are used in prediction, clustering, classification, and identification of other previously unknown data patterns. This article describes foundational concepts that relate to ANNs, including an understanding of how ANNs are linked to biological concepts and the underlying ANN families. The article includes an explanation of common ANN methods, architecture/hyperparameter determination for initializing ANNs, and current research directions. The article concludes with a discussion on the need for algorithmic transparency and repeatability of research.

Many learning management systems (LMS) used in higher education provide customizable rubrics that aid in the process of grading and providing feedback for many forms of assessments commonly used by educators today. Rapid Grade is a grading and feedback feature built into a non-commercialized LMS developed by a large, public, Midwestern university in the United States. In this research, Rapid Grade was compared to a grading and feedback system found in one of the most utilized LMS found in higher education. It should be noted that the name of this particular LMS is not named. Using the Technology Acceptance Model to validate that Rapid Grade empirically improves upon existing methods, survey results indicate that Rapid Grade is a significant improvement in terms of ease of use and usefulness when grading and providing feedback for a given assessment. The Rapid Grade framework as well as the specific results of the TAM is presented.

Decision support systems (DSS) are information systems that facilitate human decision-making through the presentation and analysis of data. Primarily, these information systems allow people to improve decisions by using additional data and information that they have access to but do not always use to make a more informed decision. DSS efforts enable military and civilian leaders to improve strategic plans and make decisions. Increasingly, DSS are seen in use by both the public and decision makers to make sense of big data, as seen with COVID-19 presentations. Artificial intelligence (AI) can be used to provide rapid interpretation of the raw data and results for use within DSS systems. DSS is colloquially termed a “dashboard” and involves three main components: the database, model, and the user interface. This article explores archetypes of DSS and aims to discuss each component in equal measure since ignoring one aspect leads to various issues (e.g., a DSS employing a good model with appropriate data but poor implementation).