Computer assisted assessment and the role it plays in educational decision-making and educational justice: a case study of one teacher training college in Zimbabwe

A ZJER research on computer - assisted assessments and educational decision-making in the Zimbabwe education system.Although the use of computers in data-driven decision making in education was initially focused on education's core business i.e. computer aided learning (CAL), educational leaders are now using this approach to transform other aspects of their operations e.g. computer-assisted assessment (CAA). The full potential of CAA has yet to be realized and its implementation within higher education can be fraught with difficulties. This paper draws on a research that was carried out in one teachers' college in Zimbabwe. The main aim was to engage with the final grading system used on the teaching practice phase ofa group of600 newly qualified teachers with a view of identifying how the computer was being used to allow humans to benefit from machine decision-making without losing the opportunity for rational thought. This was driven by a sincere conviction that better data-driven decisions in education benefit everyone, including the learners, teachers, administrators, patrons, taxpayers and the state. The researcher employed an approach commonly used in IT, which is called Data Mining. The findings seem to point to a grading system which is using a computer more as a data capture and calculation instrument without questioning the moral argument for letting the computer decide. Such a grading system has potential for loss of human autonomy and for being unfair to the subjects

Human-AI Collaboration in Content Moderation: The Effects of Information Cues and Time Constraints

An extremely large amount of user-generated content is produced by users worldwide every day with the rapid development of online social media. Content moderation has emerged to ensure the quality of posts on various social media platforms. This process typically demands collaboration between humans and AI because of the complementarity of the two agents in different facets. Wondering how AI can better assist humans to make final judgment in the “machine-in-the-loop” paradigm, we propose a lab experiment to explore the influence of different types of cues provided by AI through a nudging approach as well as time constraints on human moderators’ performance. The proposed study contributes to the literature on the AI-assisted decision-making pattern, and helps social media platforms in creating an effective human-AI collaboration framework for content moderation

An interactive human centered data science approach towards crime pattern analysis

The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making. In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme. The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph. This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime. We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis