Improving Assessment Strategies in General Chemistry

In an effort to improve assessment strategies in a general chemistry course at Valparaiso University, a new homework submission and evaluation system was implemented. Learning objectives for the course were revised to match new goals. These updated learning objectives guided the creation of homework problem sets on the LearningOnline Network with Computer-Assisted Personalized Approach (LON-CAPA). This free, open-source, distributed learning content management system provides improved homework assessment for students through immediate feedback, personalized questions, and a flexible format. Homework for the first semester general chemistry course was prepared on LON-CAPA and a simple user\u27s guide was also created for LON-CAPA to facilitate future use of the program

Case studies of personalized learning

Deliverable 4.1, Literature review of personalised learning and the Cloud, started with an evaluation and synthesis of the definitions of personalized learning, followed by an analysis of how this is implemented in a method (e-learning vs. i-learning, m-learning and u-learning), learning approach and the appropriate didactic process, based on adapted didactic theories. From this research a list of criteria was created needed to implement personalised learning onto the learner of the future. This list of criteria is the basis for the analysis of all case studies investigated. – as well to the learning process as the learning place. In total 60 case studies (all 59 case studies mentioned in D6.4 Education on the Cloud 2015 + one extra) were analysed. The case studies were compared with the list of criteria, and a score was calculated. As a result, the best examples could be retained. On average most case studies were good on: taking different learning methods into account, interactivity and accessibility and usability of learning materials for everyone. All had a real formal education content, thus aiming at the core-curriculum, valuing previous knowledge, competences, life and work skills, also informal. Also the availability of an instructor / tutor or other network of peers, experts and teachers to guide and support the learning is common. On the other hand, most case studies lack diagnostics tests as well at the start (diagnostic entry test), during the personalized learning trajectory and at the end (assessment at the end). Also most do not include non-formal and informal learning aspects. And the ownership of personalized learning is not in the hands of the learner. Five of the 60 case studies can as a result be considered as very good examples of real personalized learning

Automated tutoring for a database skills training environment

Universities are increasingly offering courses online. Feedback, assessment, and guidance are important features of this online courseware. Together, in the absence of a human tutor, they aid the student in the learning process. We present a programming training environment for a database course. It aims to offer a substitute for classroom based learning by providing synchronous automated feedback to the student, along with guidance based on a personalized assessment. The automated tutoring system should promote procedural knowledge acquisition and skills training. An automated tutoring feature is an integral part of this tutoring system

Motivational Social Visualizations for Personalized E-Learning

A large number of educational resources is now available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produces at least two problems: how to help students find the most appropriate resources, and how to engage them into using these resources and benefiting from them. Personalized and social learning have been suggested as potential methods for addressing these problems. Our work presented in this paper attempts to combine the ideas of personalized and social learning. We introduce Progressor + , an innovative Web-based interface that helps students find the most relevant resources in a large collection of self-assessment questions and programming examples. We also present the results of a classroom study of the Progressor +  in an undergraduate class. The data revealed the motivational impact of the personalized social guidance provided by the system in the target context. The interface encouraged students to explore more educational resources and motivated them to do some work ahead of the course schedule. The increase in diversity of explored content resulted in improving students’ problem solving success. A deeper analysis of the social guidance mechanism revealed that it is based on the leading behavior of the strong students, who discovered the most relevant resources and created trails for weaker students to follow. The study results also demonstrate that students were more engaged with the system: they spent more time in working with self-assessment questions and annotated examples, attempted more questions, and achieved higher success rates in answering them

Progressor: Social navigation support through open social student modeling

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students. © 2013 Taylor and Francis Group, LLC

Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment

Automated assessment of rehabilitation exercises using machine learning has a potential to improve current rehabilitation practices. However, it is challenging to completely replicate therapist’s deci sion making on the assessment of patients with various physical conditions. This paper describes an interactive machine learning approach that iteratively integrates a data-driven model with ex pert’s knowledge to assess the quality of rehabilitation exercises. Among a large set of kinematic features of the exercise motions, our approach identifies the most salient features for assessment using reinforcement learning and generates a user-specific analysis to elicit feature relevance from a therapist for personalized rehabilita tion assessment. While accommodating therapist’s feedback on fea ture relevance, our approach can tune a generic assessment model into a personalized model. Specifically, our approach improves performance to predict assessment from 0.8279 to 0.9116 average F1-scores of three upper-limb rehabilitation exercises ( < 0.01). Our work demonstrates that machine learning models with feature selection can generate kinematic feature-based analysis as expla nations on predictions of a model to elicit expert’s knowledge of assessment, and how machine learning models can augment with expert’s knowledge for personalized rehabilitation assessment.info:eu-repo/semantics/publishedVersio

Measuring the Additive Effects of Multimedia Social Cue Principles on Learners’ Cognitive Load, Emotions, Attitude, and Learning Outcomes

Multimedia principles are developed and employed to design effective multimedia instructions that foster learning. Specifically, multimedia principles such as personalization, voice, and embodiment principles are developed based on social cues to promote deep learning. Most researchers in the past have investigated the individual effects of these principles on learning. The goal of the present study was to investigate the additive effects of these abovementioned principles on learners’ perceived cognitive load, emotions, attitude, and learning outcomes (i.e. retention and transfer of knowledge). Sixty college students participated in this study. Participants were asked to complete two short instructional modules and a short learning assessment after each module. Additionally, they were asked to complete a NASA Task Load Index (TLX) questionnaire, emotion assessment, and attitude questionnaire. The results suggested that non-personalized instructions lead to higher cognitive load than the personalized instructions. Participants in the personalized voice with embodiment condition had the least feelings of disgust when learning the information and had highest retention scores. Additionally, personalized voice narrations were found to be detrimental for learning. However, if personalized voice narrations are used for instructional purposes, then it must be accompanied with an embodiment to foster learning and improve performance on transfer of knowledge. The findings of this study could be used to improve the design of the multimedia instructions that are effective in fostering learning

Evaluation of the Project Management Competences Based on the Semantic Networks

The paper presents the testing and evaluation facilities of the SinPers system. The SinPers is a web based learning environment in project management, capable of building and conducting a complete and personalized training cycle, from the definition of the learning objectives to the assessment of the learning results for each learner. The testing and evaluation facilities of SinPers system are based on the ontological approach. The educational ontology is mapped on a semantic network. Further, the semantic network is projected into a concept space graph. The semantic computability of the concept space graph is used to design the tests. The paper focuses on the applicability of the system in the certification, for the knowledge assessment, related to each element of competence. The semantic computability is used for differentiating between different certification levels.testing, assessment, ontology, semantic networks, certification.

Personalization, Cognition, and Gamification-based Programming Language Learning: A State-of-the-Art Systematic Literature Review

Programming courses in computing science are important because they are often the first introduction to computer programming for many students. Many university students are overwhelmed with the information they must learn for an introductory course. The current teacher-lecturer model of learning commonly employed in university lecture halls often results in a lack of motivation and participation in learning. Personalized gamification is a pedagogical approach that combines gamification and personalized learning to motivate and engage students while addressing individual differences in learning. This approach integrates gamification and personalized learning strategies to inspire and involve students while addressing their unique learning needs and differences. A comprehensive literature search was conducted by including 81 studies that were analyzed based on their research design, intervention, outcome measures, and quality assessment. The findings suggest that personalized gamification can enhance student cognition in programming courses by improving motivation, engagement, and learning outcomes. However, the effectiveness of personalized gamification varies depending on various factors, such as the type of gamification elements used, the degree of personalization, and the characteristics of the learners. This paper provides insights into designing and implementing effective personalized gamification interventions in programming courses. The findings could inform educational practitioners and researchers in programming education about the potential benefits of personalized gamification and its implications for educational practice

The Impact of Vocabulary Assessment and Personalized Feedback on Students’ Vocabulary Mastery

This review explores the synergy between vocabulary assessment and personalized feedback in supporting students’ vocabulary mastery and enhancing their learning experiences. Vocabulary plays a crucial role in academic success, serving as the cornerstone of comprehension and communication. Therefore, accurate vocabulary assessment and effective feedback mechanisms are imperative. The paper outlines the significance of individualized learning, emphasizing the need to recognize students’ unique learning styles and tailor feedback accordingly, and discusses the transformative role of technology in facilitating innovative assessment and feedback approaches. However, the implementation of these approaches encounters various challenges, including technical barriers, logistical hurdles, and resistance from educators and students. The current body of research, while insightful, also presents limitations such as restricted scope, scale, and unaddressed gaps in knowledge. Despite these challenges, the integration of vocabulary assessment and personalized feedback offers promising prospects for enhancing students’ learning outcomes and motivation. Future research needs to focus on overcoming existing challenges and expanding the understanding of this integrative educational approach to benefit diverse student populations