Modeling peer assessment as a personalized predictor of teacher's grades: The case of OpenAnswer

Questions with open answers are rarely used as e-learning assessment tools because of the resulting high workload for the teacher/tutor that should grade them. This can be mitigated by having students grade each other's answers, but the uncertainty on the quality of the resulting grades could be high. In our OpenAnswer system we have modeled peer-assessment as a Bayesian network connecting a set of sub-networks (each representing a participating student) to the corresponding answers of her graded peers. The model has shown good ability to predict (without further info from the teacher) the exact teacher mark and a very good ability to predict it within 1 mark from the right one (ground truth). From the available datasets we noticed that different teachers sometimes disagree in their assessment of the same answer. For this reason in this paper we explore how the model can be tailored to the specific teacher to improve its prediction ability. To this aim, we parametrically define the CPTs (Conditional Probability Tables) describing the probabilistic dependence of a Bayesian variable from others in the modeled network, and we optimize the parameters generating the CPTs to obtain the smallest average difference between the predicted grades and the teacher's marks (ground truth). The optimization is carried out separately with respect to each teacher available in our datasets, or respect to the whole datasets. The paper discusses the results and shows that the prediction performance of our model, when optimized separately for each teacher, improves against the case in which our model is globally optimized respect to the whole dataset, which in turn improves against the predictions of the raw peer-assessment. The improved prediction would allow us to use OpenAnswer, without teacher intervention, as a class monitoring and diagnostic tool

Rich environments for active learning: a definition

Rich Environments for Active Learning, or REALs, are comprehensive instructional systems that evolve from and are consistent with constructivist philosophies and theories. To embody a constructivist view of learning, REALs: promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making, and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher-order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning-to-learn within authentic contexts using realistic tasks and performances. REALs provide learning activities that engage students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, REALs are a response to educational practices that promote the development of inert knowledge, such as conventional teacher-to-student knowledge-transfer activities. In this article, we describe and organize the shared elements of REALs, including the theoretical foundations and instructional strategies to provide a common ground for discussion. We compare existing assumptions underlying education with new assumptions that promote problem-solving and higher-level thinking. Next, we examine the theoretical foundation that supports these new assumptions. Finally, we describe how REALs promote these new assumptions within a constructivist framework, defining each REAL attribute and providing supporting examples of REAL strategies in action

A review of the Development Trend of Personalized learning Technologies and its Applications

Personalized learning tailors material and strategy to student requirements, interests, and goals in e-learning. These developments help educational institutions and other organizations to keep up with the fast pace of information technology, communications, and computing power. Studies show that self-adaptive learning and relevant learning information improve study efficiency. Compared to traditional teaching methods, the practice of online education is well in its infancy. On the other hand, the pedagogy and evaluation of students in online courses have a large gap that has to be filled, necessitating significant improvements in e-learning. We call this approach to education "personalized learning," which is a central focus of today's leading online education platforms. Several studies have been conducted on e-learning and personalized learning, but few investigated the development trend of personalized learning technologies and applications. Therefore this study examines the literature to close the gap and promote the development trend for personalized learning technologies and applications in higher education from 2010 to 2021 by analyzing related journal articles. The pivotal studies used inclusion criteria after a search generated 372 complete research articles and reduced them to 146 publications based on their proposed learning domains and research themes. Through carefully reviewing current trends and successes in numerous aspects of personalized learning, this discussion analyzes prospective future research directions in the field of personalized learning

Integration of evolutionary algorithm in an agent-oriented approach for an adaptive e-learning

This paper describes an agent- oriented approach that aims to create learning situations by solving problems. The proposed system is designed as a multi-agent that organizes interfaces, coordinators, sources of information and mobiles. The objective of this approach is to get learners to solve a problem that leads them to get engaged in several learning activities, chosen according to their level of knowledge and preferences in order to ensure adaptive learning and reduce the rate of learner abundance in an e-learning system. The search for learning activities procedure is based on evolutionary algorithms typically: genetic algorithm, to offer learners the optimal solution adapted to their profiles and ensuring a resolution of the proposed learning problem. In terms of results, we have adopted “immigration strategies” to improve the performance of the genetic algorithm. To show the effectiveness of the proposed approach we have made a comparative study with other artificial intelligence optimization methods. We conducted a real experiment with primary school learners in order to test the effectiveness of the proposed approach and to set up its functioning. The experiment results showed a high rate of success and engagement among the learners who followed the proposed adaptive learning scenario

Strengths and Limitations of SmallTalk2Me App in English Language Proficiency Evaluation

This paper explores the strengths and limitations of the SmallTalk2Me App, an AI-driven language assessment tool, in evaluating English language proficiency. The study adopts a mixed-method approach, combining interviews with three experienced English teachers and a comprehensive literature review to provide a comprehensive analysis of the app's performance. The research begins with an exploration of the app's strengths, which include its objective and consistent evaluation metrics. The app's automated nature ensures that all test takers are assessed based on the same predefined criteria, reducing human bias and enhancing the reliability of evaluations. Also, it offers immediate feedback, allowing learners to identify their areas of improvement promptly and adapt their learning strategies accordingly. Conversely, the limitations of the SmallTalk2Me App are also discussed. One notable limitation is the challenge of replicating the complexity of real-life communication contexts. App-based assessments may not fully capture the intricacies of natural conversations. Additionally, the app's pronunciation assessment may struggle with accurately recognizing variations in accents and speech patterns, leading to potential inaccuracies in pronunciation evaluation. The insights from the interviews and literature review contribute to a comprehensive understanding of the app's performance, offering valuable implications for its effective use in language teaching and learning settings

AI in Student as Manager Model Future Directions of Business Studies

In the business programs of Universiti Pendidikan Sultan Idris (UPSI), the Three-Pronged teaching technique is implemented as a student-centered learning process. This approach combines elements of the game, problem, and challenge-based learning with the larger goal of preparing business students to handle complicated, unanticipated global or industrial problems. It promotes an interactive and dependable classroom that calls for students' innovative contributions, teamwork, and participation in the professional world. Micro credential platforms, artificial intelligence, and a new pedagogical strategy: that's the idea for UPSI's undergraduate business. Therefore, this kind of instruction is increasingly being used in business courses like Strategic Management. Undergraduate students benefit from this teaching method since they are exposed to industrial phenomena while developing 21st- century abilities (collaborative, creative, critical thinking, and communication)