STUDENT LEARNING ASSESSMENT

STUDENT LEARNING ASSESSMEN

Teachers learn about student learning assessment through a teacher education process

This study aims to understand the extent to which university professors adopt new pedagogical voices in their learning assessment practices through a teacher education process. Participants (N = 32) were interviewed before and after the teacher education process, and data were analysed using qualitative and quantitative methods. The results of the study demonstrated, first, that teachers renamed their educational discourse about learning assessment significantly, increasing it in assessment for learning practices, particularly in the themes of timing and agents, and reducing it in all themes referred to the assessment of learning practices. And second, three clusters of faculty were identified, which differed in terms of the way they merge both learning assessment practices: professors with a slight prevalence of the assessment for learning conceptual voice, professors with a slight prevalence of the assessment for learning practical voice, and professors with a strong prevalence of the assessment for learning voice

Rubric for Evaluating Institutional Student Learning Assessment Processes

Rubric for Evaluating Institutional Student Learning Assessment Processe

Student Assessment Conference Planned for Oct. 25-26 at GWU

The Gardner-Webb University Godbold School of Business presents its inaugural Student Learning Assessment Conference Oct. 25-26 from 9 a.m. to 12:30 p.m. in Hamrick Hall’s Blanton Auditorium. The event is designed to provide professional development for faculty, staff, and administrators in order to help improve student learning outcomes. The Student Learning Assessment Conference will showcase such topics as student learning outcomes made simple; the assessment process: how does it all fit together; and the ABC’s of assessment rubrics.https://digitalcommons.gardner-webb.edu/gardner-webb-newscenter-archive/2751/thumbnail.jp

Prediction tools for student learning assessment in professional schools

Professional Schools are in need to access technologies and tools that allow the monitoring of a student evolution course, in acquiring a given skill. Furthermore, they need to be able to predict the presentation of the students on a course before they actually sign up, to either provide them with the extra skills required to succeed, or to adapt the course to the students’ level of knowledge. Based on a knowledge base of student features, the Student Model, a Student Prediction System must be able to produce estimates on whether a student will succeed on a particular course. This tool must rely on a formal methodology for problem solving to estimate a measure of the quality-ofinformation that branches out from students’ profiles, before trying to guess their likelihood of success. Indeed, this paper presents an approach to design a Student Prediction System, which is, in fact, a reasoner, in the sense that, presented with a new problem description (a student outline) it produces a solved problem, i.e., a diagnostic of the student potential of success

Assessment Document Writers Group

The Office of Institutional Effectiveness is offering a new program this summer geared toward any assessment coordinators looking for camaraderie and support as they work through the development of annual student learning assessment documents

Artificial intelligence tools for student learning assessment in professional schools

The necessity to maximize the learning success of the students as well as to produce professionals with the right skills to fulfil the market requirements, raises the question of closely following and assessing the learning paths of the students of Professional Schools. To solve at once problems and difficulties that arise during the learning process, we need to develop technologies and tools that allow the monitoring of those paths, if not in real time, at least periodically. Supported on a knowledge base of student features, also called a Student Model, a Student Assessment System must be able to produce diagnosis of student’s learning paths. Given the wide range of students’ learning experiences and behaviours, which implies a wide range of points and values in students’ models, such a tool should have some sort of intelligence. Moreover, that tool must rely on a formal methodology for problem solving to estimate a measure of the quality-ofinformation that branches out from students’ profiles, before trying to diagnose their learning problems. Indeed, this paper presents an approach to design a Diagnosis Module for a Student Assessment System, which is, in fact, a reasoner, in the sense that, presented with a new problem description (a student outline) it produces a solved problem, i.e., a diagnostic of the student learning state. We undertook the problem by selecting the attributes that are meaningful to produce a diagnosis, i.e., biographical, social, economical and cultural data, as well as skills so far achieved, which may drive, as constraints or invariants, the acquisition of new knowledge. Next, we selected the metrics that would allow us to infer the quality of the ongoing learning, i.e., the degree of expertise on the currently attended learning domains. To collect these indicators we used the Moodle e-Learning System. Both, attributes and metrics, make the student model. Finally, we designed a reasoner based on Artificial Intelligence techniques that rely on the Quality-of-Information quantification valuations to foster a Multi-Valued Extended Logic Programming language, a key element in order to produce diagnosis of the student learning paths. Confronted with a new case, i.e., a student model, the reasoner evaluates it in terms of its QI and outputs a diagnostic

Gaining Institutional Efficiency in the General Education Assessment for Technology Program Accreditation

This work addresses the need to merge various student learning assessment activities to gain efficiency. Specifically, it discusses a mechanism of institutional assessment efficiency through development of common instruments for general education assessment that can support more than one accreditation requirement