Search

Refereed/Peer-reviewed Over the past decade, the increasing use of learning analytics opened the possibility of making data-driven decisions for improving student learning. Driven by the strong university adoption of learning analytics, most early learning analytics research focused on issues specific to tertiary education. With the broader adoption of educational technologies in primary and secondary education and the emergence of new classroom-focused technologies, there has been a growing awareness of the potentials of learning analytics for supporting students and diagnosing their learning progress in pre-university contexts. This special section focused on investigating, developing, and evaluating state-of-the-art learning analytics approaches within primary and secondary school settings. In this editorial, we summarize the papers of the special section and discuss the challenges and opportunities for learning analytics within the school context. We conclude with the discussion around the opportunities for future work and the implications of this special section for the field of learning analytics.

Learning using the Internet has become a vital factor for academic success in higher education. Students increasingly rely on the Internet as their main information source. However, related researc...

Artificial intelligence (AI) applications continue to improve decision-making processes at all levels of education. A relatively untouched area in which AI can be quite useful is automating assessment-related decisions about students’ learning outcomes when monitoring students’ learning progress through computerized formative assessments. While the use of computerized formative assessments in the classroom allows teachers to assess students’ learning continuously and more frequently, there are implementation-related barriers that prevent teachers from maximizing the diagnostic value of such assessments. For example, the administration frequency and scheduling of computerized formative assessments are highly critical for students regardless of their performance level. Traditionally, most teachers have to rely on their judgment and observations in determining the testing schedule for all students. Human judgments, however, might be highly subjective given that teachers are not likely to oversee each student’s academic history closely. In this study, we aim to introduce a deep learning framework to predict and optimize the number of test administrations and support the decisions using clustering approaches. We used math performance data gathered from 10,107 first graders during the 2017–2018 school year. Our best model demonstrated highly accurate prediction results with average accuracy scores close to 90%. In addition, the clustering approach revealed interpretable insights into how the test administration decisions were associated with students’ performance profiles. The proposed system would greatly help teachers make more systematic and informed test administration decisions and thereby maximize the effectiveness of computerized formative assessments in promoting student learning.

The acquisition of warranted domain-specific knowledge is essential for practical work in medicine. The medical field, however, suffers from 'information overload', and students and physicians rely...

Educational robotics has proven its positive impact on the performances and attitudes of students. However, the educational environments that employ them rarely provide teachers with relevant information that can be used to make an effective monitoring of the student learning progress. To overcome these limitations, in this paper we present IDEE (Integrated Didactic Educational Environment), an educational environment for physics, that uses EV3 LEGO Mindstorms® educational kit as robotic component. To provide support to teachers, IDEE includes a dashboard that provides them with information about the students’ learning process. This analysis is done by means of an Additive Factor Model (AFM). That is a well-known technique in the educational data mining research area. However, it has been usually employed to carry out analysis about students’ performance data outside the system. This can be a burden for the teacher who, in most cases, is not an expert in data analysis. Our goal in this paper is to show how the coefficients of AFM provide valuable information to the teacher without requiring any deep expertise in data analysis. In addition, we show an improved version of the AFM that provides a deeper understanding about the students’ learning process.

Due to COVID-19, non-F2F (non-face-to-face) learning is being conducted in educational sites around the world. Unlike theoretical subjects, which have a variety of applicable non-F2F learning content, in practical subjects, learners may undergo many difficulties due to many learning activities. Therefore, this research tries to design and develop a mobile application that allows learners to perform a self-diagnostic on their learning progress. In this research, we conducted a mobile application usability evaluation for 36 students who participated in non-F2F Arduino practice learning. To this end, we applied the ADDIE (Analysis, Design, Development, Implement, and Evaluation) model to develop a self-diagnostic mobile application for students’ learning progress according to the learning content. The research tool modified and distributed the question item appropriate for this subject’s study for the mobile usability evaluation that was used in the previous study. This research applied the ADDIE model to analyze the characteristics of students and learning contents, including designed learning contents, database, menu structure, developed learning contents, and mobile application. After using this in non-F2F practice learning for 15 weeks, a mobile application usability evaluation was conducted. As a result of the study, due to receiving usability scores between 3.53 and 4.42, it was found that the learning progress self-diagnostic in non-F2F practice learning was essential and that mobile applications were useful in non-F2F practice learning. Additionally, 33 out of 36 students responded that it would be useful for their learning if they actively used it in other subjects as well. We have found that leveraging self-diagnostic mobile applications through the mobile application usability evaluation tool can be useful for non-F2F practice learning. Additionally, it is expected to be useful for non-F2F practice learning if additional research is conducted on other factors as well such as teaching presence, online learning engagement, and learning flow in the future.

In this paper, a four-legged robot is trained to walk in the real world, without any manual engineering or programmed movement sequences. The goal is to enable robots to learn to walk on their own using reinforcement learning algorithms. Since each leg has three joints and the robot has four legs in total, this is a very complicated behavior to learn, so doing the training process in the real world would not be feasible. To accelerate the learning progress, an accurate simulated environment is used in which the robot can safely be trained using state-of-the-art learning algorithms, such as soft-actor critic and proximal policy optimization. The learnt behavior has then successfully been transferred to the real robot, with the real robot mirroring the behavior of the robot in the simulation. This resulted in the real robot moving forward.

Learning analytics (LA) mines, analyzes and visualizes the data of students’ learning behaviours on learning platforms such as Learning Management Systems (LMS), but few LA tools built are adaptable to multiple platforms or for general education courses. This study sets out to evaluate a lightweight LA tool implemented on Moodle and Open edX for monitoring students’ learning progress. Survey data were collected from 156 students, supplemented by interview responses from 25 students and three instructors. Preliminary results show that a considerable portion of surveyed students used the LA tool and they held positive opinions on its efficacy in monitoring self-progress and the effectiveness of its visualizations for information delivery. Nonetheless, learners who did not use the LA tool raised concerns about it relying only on their online behaviours without considering their offline learning. Coupled with instructors’ evaluation results, discussion and implications are presented.

INTRODUCTION Making use of 3D printed teeth models in teaching students offers an innovative approach. The mistakes made by the students at the access cavity stage were assessed with the aid of 3D models, and their overall, hands-on learning progress was evaluated. MATERIAL AND METHODS Ninety 3D models of teeth were constructed using stereolithographic technology and then randomly divided into 9 groups. One dentistry student was randomly assigned to each group and then performed primary access cavity in 10 identical 3D models. Then the teeth were evaluated in the order of their preparation, relative to the model tooth. RESULTS The material of 14 (15.5%) out of 90 teeth models sustained significant damage during the preparation. As regards the remaining 76 (84.5%) 3D models, the students committed the greatest number of mistakes on the incisors, and fewer on the molars and the least in the premolars. The difference in the number of errors between particular groups of teeth was statistically significant (P = .0001). The number of errors committed in subsequent repetitions amongst all students was significantly different for the incisors (P = .00215) and premolars (P = .00383), whereas insignificant in the case of molars (P = .77116). CONCLUSIONS Thanks to perfect representation of teeth anatomy; making use of 3D models in the teaching of endodontics may well be recommended as holding substantial potential in improving overall quality of training at the pre-clinical stage, with a view to appreciably reducing overall risk of encountering complications during the actual clinical work.

The teacher needs to know the learner's progress so that he/she can improve her/his teaching strategy. The initial challenges faced by the teachers are including the need to monitor each learner's progress so that their needs and difficulties can be recognized as a guide for enhancing learning strategies. One way to solve these problems is to predict student's progress. Mathematics in elementary school is one of the subjects with a high failure rate of learners. In this study, the progress of learners was predicted using Random Forest based on classification using similarity characteristics of learners obtained from the results of previous formative assessment. The random forest algorithm was used because it can classify data that has incomplete attributes, which are usually contained in learner characteristics. Prediction models are built based on data of assessment results from 2 math classes with 46 students in elementary school. The resulting model performance will be measured using accuracy and recall because False Positive is better than False Negative so learning progress will always be improved. The results show that the Random Forest algorithm can create a learning progress prediction model with an accuracy of 90% in training data and 93% in testing data.

Supporting educational processes with Hidden Markov Models (HMMs) has great potential. In this paper, we explore the possibility of identifying students’ learning progress with HMMs. Students’ grades are used to train the HMMs to find out if the analysis of obtained models lets us detect patterns emerging from student’s results. We also try to predict the final students’ results on the basis of their partial grades. A new, classification approach for this problem, using properties of HMMs is proposed: High and Low State Model (HLSM).

This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system. This paper proposes an effective continual learning for the task-oriented dialogue system with iterative network pruning, expanding and masking (TPEM), which preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks. Specifically, TPEM (i) leverages network pruning to keep the knowledge for old tasks, (ii) adopts network expanding to create free weights for new tasks, and (iii) introduces task-specific network masking to alleviate the negative impact of fixed weights of old tasks on new tasks. We conduct extensive experiments on seven different tasks from three benchmark datasets and show empirically that TPEM leads to significantly improved results over the strong competitors. For reproducibility, we submit the code and data at: https://github.com/siat-nlp/TPEM, Comment: Accepted by The Annual Meeting of the Association for Computational Linguistics (ACL), 2021

Partial-Label Learning (PLL) aims to learn from the training data, where each example is associated with a set of candidate labels, among which only one is correct. Existing PLL methods to deal with such problem usually treat each training example equally and few works take the complexities of training examples into consideration. In this paper, inspired by the human learning mode that gradually learns from “easy” to “hard”, we propose a novel Self-Paced Curriculum strategy based Partial-Label Learning (SPC-PLL) algorithm, where curriculum strategy can predetermine prior knowledge to adjust the learning priorities of training examples, while self-paced strategy can dynamically select “easy” training examples for model induction according to its current learning progress. The combination of such two strategies is analogous to “instructor-student-collaborative” learning mode, which not only utilizes prior knowledge flexibly but also effectively avoids the inconsistency between the predetermined curriculum and the dynamically learned models. Extensive experimental comparisons and comprehensive ablation study demonstrate the effectiveness of such strategy on solving PLL problem.

This paper aims at solving the electrical engineering teaching problems caused by the new coronavirus pneumonia epidemic. The theory of Outcome Based Education (OBE) is studied Guide the online teaching work. Combined with professional features, different online teaching methods will be introduced. The advantages and disadvantages of each method will be compared. Through practice and exploration, online teaching has achieved good result. The students' learning progress is not affected by the Epidemic.

Feedback is a crucial element in helping students identify gaps and assess their learning progress. In online courses, feedback becomes even more critical as it is one of the resources where the teacher interacts directly with the student. However, with the growing number of students enrolled in online learning, it becomes a challenge for instructors to provide good quality feedback that helps the student self-regulate. In this context, this paper proposed a content analysis of feedback text provided by instructors based on different indicators of good feedback. A random forest classifier was trained and evaluated at different feedback levels. The results achieved outcomes up to 87% and 0.39 of accuracy and Cohen's κ, respectively. The paper also provides insights into the most influential textual features of feedback that predict feedback quality.

Child development at an early stage is important for learning progress. New parents need to learn to observe the development of those children. However, it requires knowledge and expertise skills to discover and monitor. This study investigated child development knowledge extraction that is appropriate for a new parent. The child development detection system has been designed by adopting the knowledge engineering method with user experience and high-fidelity prototyping design. This study shows the positive satisfaction result of a new parent taking part in these experiments to confirm on how to develop a good child development detection system in further step.

The cloud real-time interactive response system (IRS) is an important guarantee for the high-quality teaching, which helps teachers to obtain the comprehensive and timely information about students’ learning progress. The establishment of the IRS system can effectively promote students’ learning initiatives. Teachers can immediately detect students’ learning process, analyze students’ feedback through various feedback statistics, and adjust the teaching contents accordingly. This paper, taking the college English reading teaching platform as an example, explores the setting strategy of the cloud IRS system for the college English reading teaching platform, and applies the interactive response system reasonably in order to provide effective guarantee for the improvement of the college English reading teaching level.

The main objective of pathophysiology teaching is to facilitate the learning of mechanisms of diseases and the understanding of their expressions in patients (symptoms, signs and tests). This objective requires the application of basic biomedical science to explain the abnormalities expressed by the patients. The capacity to integrate this new organization of knowledge is essential to the understanding of pathophysiological mechanisms, which explain expressions of specific diseases. Our group has a longstanding experience in the teaching of pathophysiology to medical students using problem-based learning (PBL) and concept mapping (CM). This semestral discipline has a yearly intake of 190 students, divided into 18 tutorial groups and supported by 14 tutors. The students’ learning progress is evaluated by their performance during the tutorial sessions and the CM methodology has been introduced as an additional tool to visualize the integration of knowledge and how it is displayed in the different pathophysiological mechanisms. Until now, the evaluation of CM has been qualitative and used as an additional assessment tool by the tutors. This study reports how we are changing this approach by training the tutors and developing a scoring methodology, which will be described in detail, together with a preliminary application in selected maps

Affective computing is becoming more and more important as it enables to extend the possibilities of computing technologies by incorporating emotions. In fact, the detection of users’ emotions has become one of the most important aspects regarding Affective Computing. In this paper, we present an educational software application that incorporates affective computing by detecting the users’ emotional states to adapt its behaviour to the emotions sensed. This way, we aim at increasing users’ engagement to keep them motivated for longer periods of time, thus improving their learning progress. To prove this, the application has been assessed with real users. The performance of a set of users using the proposed system has been compared with a control group that used the same system without implementing emotion detection. The outcomes of this evaluation have shown that our proposed system, incorporating affective computing, produced better results than the one used by the control group. This research work has been partially funded by the regional project of JCCM with reference SBPLY/17/180501/000495, by the Scholarship Program granted by the Spanish Ministry of Education, Culture and Sport, and the predoctoral fellowship with reference 2017-BCL-6528, granted by the University of Castilla-La Mancha.

Thanks to the rapid development of spatial technology, people can now easily access global geographic information. Geography education in schools faces the need to integrate spatial technology with fieldwork. The purpose of this research is to combine geospatial information and fieldtrips to promote elementary-school students’ learning in the national geography curriculum. During a GIS camp, students were taught to use Google Earth, Google Maps and desktop GIS. They were then guided to do fieldwork and mapping of their local area so that they would know how to geotag interesting local spots by themselves. Observation and questionnaires were used to understand the students’ learning progress. Sketch-map analysis was adopted to evaluate the development of their map skills and geography capability. The results indicated that students’ map skills and geography knowledge increased. Students actively participated in the activities and showed a positive attitude towards learning using Google Maps and Google Earth rather than GIS software.

In accordance with the increasing attention in entrepreneurship among tourism and hospitality, this study developed an innovative entrepreneurship curriculum specifically designed for undergraduate hospitality students. This study developed and evaluated creative module comprised of mentoring and creative problem solving (CPS) methods for undergraduate hospitality innovative entrepreneurship programs in Taiwan. A quasi-experimental design, with a sample of 122 students, estimated the causal impacts of educational intervention. Students in the experimental group participated in a mentoring and CPS-based course for a semester. The results showed that students who received the intervention significantly enhanced their innovative entrepreneurship as assessed by their GET2test scores. This study examined innovative entrepreneurship through qualitative and quantitative instruments. The findings support the positive impact of education on the entrepreneurial potential. Moreover, an innovative entrepreneurship cultivation model was drawn up that can provide the contributions of instructional content and learning progress that can help educators be well-prepared for the course and can predict students’ progress.

Most students of English Education Program in STKIP PGRI Ponorogo can be considered as nontraditional students with family, work, and other life responsibilities which have many influences on their educational goals. Autonomous learning is a customized way of learning applied by individual to find out his own learning needs and learning goals, decide the most appropriate way for his own learning, as well as to monitor and evaluate the learning progress. This research used qualitative descriptive method. The primary techniques in data collection were questionnaire, and interview. In general, the most influential motivation for the students was internal motivation. Advisor’s availability, text book, internet, supportive atmosphere, self discipline, cozy and quiet place, and early morning were among the students’ choices for their autonomous learning. They frequently used music, film, video, essay, and smart phone application in their study. In general, the nontraditional students with high achievement were often felt easy to understand the material especially when it was delivered in relatively “easy” language, expressed their opinion bravely, and patient in doing their tasks. They considered friends, best friends, and lecturer as people who were capable to help them in doing autonomous learning

With the help of the APP learning platform, learners can make the real-time and seamless access to the top-quality curriculum resources, community education resources and teaching resources of the open education platform, and obtain the relevant information of the educational administration systems, including the learning achievement, the learning progress and the examination information. The App has not only stimulated the students’ initiatives and enthusiasms in learning, but also properly solved the existing problem of the information island. This makes it possible for learners to carry out the debris learning in any place and at any time, which provides a reference for other colleges to develop their APPs.

Learning analytics have become a well-considered aspect of modern digital learning environments. One opportunity of learning analytics is the use of learning process data enabling lecturers to analyse students’ learning progression as well as to identify obstacles and risks. With this analytics knowledge, lecturers may want to scaffold students’ learning activities to improve the learning progress and overcome obstacles or risks. Prompts are known to be a possible solution for such scaffolding mechanics. However, implementing prompts into existing legacy systems in learning environments with high data privacy concerns is quite a challenge. This research shows how a prompting application has been implemented into an existing university environment by adding a plug-in to the local digital learning platform which injects user-centric prompts to specific objects within their digital learning environment. The prompts are dynamically loaded from a separate learning analytics application which also collects the students’ learning trails and progress. The system is evaluated in two units in the fall semester 2017 with more than 400 students altogether. The system collects up to two thousand student events per day. An in-depth empirical investigation on how various prompts influence students’ learning behaviours and outcomes is currently conducted.

In this research, the learning patterns and behavioural data of learners' online learning in a cloud computing environment are analysed, and the learners' learning progress, learning rules, and lea...

The article discusses teaching methodology for older adults within: planning educational activities, defining its purposes, methods, contents, knowledge acquisition and learning progress evaluation. Special attention was paid to the formulated principles of teaching seniors.

Objective To evaluate the efficacy and the learning curve of the endoscopic ultrasound-guided fine needle aspiration biopsy (EUS-FNAB) diagnostics of pancreatic lesions over 8 years (2007–2014). Material and methods EUS-FNAB using a Linear Echoendoscope Olympus was performed in 531 patients, mostly without rapid on-site evaluation (ROSE). Smears plus cytoblock sections were used routinely. Immunocytochemistry was utilised as indicated. Results The average, satisfactory sampling rate increased from the 61.2% in the first 3-year period to 72.9% in the last 3-year period (P = 0.008). The availability of the material for cytoblocks increased from 36.4% in the first period to 75.3% in the last period (P = 0.017). The efficacy of cytoblocks increased from 39.6% to 46.2% (P = 0.086). Comparing the first and last 3-year periods, the indication for immunocytochemistry did not rise substantially (5.2% and 8.5% respectively), but the predictive value of immunocytochemistry rose from 56.3% to 100.0% (P = 0.001). The most frequent diagnostic result was malignancy confirmation – both primary (41.2%) and metastatic (2.1%). In cases with representative samples and follow-up information, the specificity, sensitivity, positive predictive value and accuracy were high from the beginning. The negative predictive value decreased slightly in the last 3-year period. Conclusion The pancreatic EUS-FNAB without ROSE represents a suboptimal arrangement conditioned with our staff/time/location reality. Nevertheless, within the last 3 years of our activity, nearly three-quarters of patients profit from the diagnostic contribution of this procedure.

Stealth assessment derives the progression of learning in an unobtrusive way from observed gameplay captured in log files. To this end, it uses machine learning technologies to provide probabilistic reasoning over established latent competency variable models. Now that video games are increasingly being used for training and learning purposes, stealth assessment could provide an excellent means of monitoring learning progress without the need for explicit testing. However, applying stealth assessment is a complex and laborious process. This paper analyses the limitations of stealth assessment and conceptualizes the requirements for developing a generic tool that could overcome its barriers and accommodate its practical application. Hence, a framework is presented describing its user and functional requirements. The proposed generic solution could open up the wider uptake of stealth assessment in serious games.

The aimed of this study is to investigate the impact of social media utilization on the student's performances for knowledge sharing in teaching and learning progress. A research model on the basis of the Task-Technology Fit Theory and three hypotheses theory was developed for this study. Model and hypotheses then tested and validated using data obtained from a survey of respondents. The survey was conducted on students at a university in Indonesia. Of the 103 questionnaires filled out by members of the university, 75 questionnaires declared valid and used for further analysis. Data were analyzed using Partial Least Square (PLS) PLS Smart software utilizes V2. This study reveals that student performance in sharing knowledge with social media impact by technology characteristic and social media utilization.

Student academic evaluation is part of the learning process in order to control the student's learning progress. The evaluation will show whether the student will pass or fail and for an instructor to guide for future evaluations on performance. There are criteria such as student's gender, student's age when they are registered in university, student's 1st semester GPA, etc., which exist in academic data can be utilized to get the student academic performance using multicriteria decision making. Multi-Objective Optimization by Ratio Analysis (MOORA) and Simple Multi-Attribute Rating (SMART) was two simple technique in multi-criteria decision making that the criteria weight can be determined objectively using entropy and gain values. This paper tries to evaluate the student academic performance using MOORA and SMART with criteria weight and sub-criteria weight resulted from entropy and gain. Decision output out of MOORA and SMART then compared with actual data using confusion matrix to discover the performance of those criteria and sub-criteria weight. The result showed that the performance of criteria weight with accuracy was 60.9 percent and the criteria of fourth-grade point average have the biggest impact on student academic evaluation with 0.1589 of weight. The result of this research can be used to help the instructor to determine the weight of student criteria for future recommendations and evaluations on student performance.

Using a laptop or tablet computer can be challenging for senior users, but using these devices can facilitate communication with family and care-givers. The goal of the current study is to get more insight in how to instruct and train senior users to use the keyboard of a tablet computer that is specifically designed for seniors (Compaan). Research has suggested that addressing errors and showing how to correct these errors can have positive effects on the learning process. Two series of video instructions were developed. In the first series, it was explained how to use the tablet computer without addressing potential errors. In the second series, potential errors were addressed. Forty seniors (75 – 97 years of age) participated in this study. The participants were asked to familiarize themselves with the Compaan, with the help of one of the series of instructional videos. Their learning progress, task effectiveness, and their self-efficacy and satisfaction levels were measured. The results showed that most participants were interested and motivated to learn to use the tablet computer. Participants who watched the videos in which potential errors were addressed performed somewhat better than the other group.

The evolution of learning technology and tools changed the way students access information and build their knowledge. Registering the interaction of students with these tools generates a large amount of data that, once critically analysed, can provide important clues about the students’ learning progress. Nevertheless research has still to be conducted to fully understand how (and if) the students’ interaction with the learning technologies relates to their learning success. In parallel, new analytical tools must be developed to allow teachers to fully exploit the information embedded in this data, in a friendly but flexible way. This article is a contribution to this effort and presents a study where the use of a learning management system (LMS) in a specific semester-long course in an Engineering school produced data that was analysed and correlated to the students’ success. Results indicate that some correlation exists between the effective use of some of the tools integrated in the LMS and student success which points the way to buld specific applications to provide teachers with indicators of students in danger of failing.

Nowadays Information Technology (IT) has been engaged in all spheres of life. It plays an important role in developing and processing works in all types of organizations, especially in the teaching process in institutions and universities. The purpose of this paper is to present the impact of information technology in the process of learning progress and teaching improvement in the University of Sulaimani from both lecturers and students perspective view, also to determine the common key factors which teaching process relies upon the information technology framework. In this paper, the researchers created an online questionnaire survey and took a sample number of academic staff and students in different colleges and departments of the University of Sulaimani in 2017, which were 320 questionnaires. The paper shows that information technology has become a basic need not dispense within the teaching process in universities and institutions in this era, also emphasize that various level of understanding of Information Technology serves various learning and teaching process.

The study investigates the usefulness of weblogs in evaluating the understanding and performance of Public Relations (PR) learners. The study also explored PR leaners’ reactions towards using weblogs as a learning and assessment tool for their performance. Fifty undergraduate students from a private institution of higher learning in the Klang Valley participated in a semester-long weblog project. The task required students to complete eight weekly-writing reflection entries on the principles and practices of PR onto their individual weblogs. The results indicate that PR leaners’ understanding of the weekly topics improved with the use of weblogs. Moreover, 98% of the participants considered their blogging experience in PR-related topics positive. The majority of students believed that the weekly-writing reflections using weblogs constituted a good tool to showcase their learning process and help them reflect on their learning progress in a weblog-based environment. 96% welcomed the idea of employing blogs as an assessment tool. These results carry pedagogical implications in the use of technology for performance and assessment of PR learners.

This study proposes that learning is a process of transitioning from one stage to another stage within a knowledge base that features concepts and relations. Drawing on the theories of expertise, this study explored two different models of learning progress (i.e., three- and two-stage models) in the context of classroom learning and identified a model that was a good fit to the data. Participants in this investigation included 136 students and 7 professors from six different universities in the United States. In order to detect and validate stages of learning progress in participants' written responses to an ill-structured and complex problem scenario, this study utilized Exploratory Factor Analysis (EFA) and the Continuous Log-Linear Cognitive Diagnostic Model (C-LCDM) method (Bozard, 2010). The results demonstrate that the three latent classes matched the three stages of the three-stage model. This study provides an account of a diagnostic model of learning progress and associated assessment methods, yet further studies are required to investigate different conditions.

It is widely believed that parents' involvement in students' music learning is vital to their success. Shinichi Suzuki not only provided teachers with useful and logical teaching skills, but also stressed on the importance of the parents as a “home teacher”. To enhance the interactions among parents, students, and teachers as well as encourage parental participation, this paper proposes a Web 2.0 learning platform (WASSAP) with an Alert Management System (AMS) as a solution to tackle problems of busy parents who do not have adequate time to involve in students' music learning. The system can facilitate busy parents to pay more attention to students' learning progress, with the AMS managing the underlining communication among various parties involved.

The study investigated the usefulness of blogs in evaluating English-as-a-Foreign-Language learners' oral performance and explored their reactions toward using blogs as a learning and assessment tool for English-speaking performance. Fifty-one college students participated in a semester-long blog project, and each uploaded five audio entries to their individual blogs. The results indicated that the scores on the speaking blogs served as a significant predictor of students' oral presentation performance. Moreover, more than half of the participants considered their blogging experience positive. Close to half believed that the speaking blog constituted a good tool to showcase their learning process and help them reflect on their learning progress in the blog-based environment. However, only a few welcomed the idea of employing blogs as an assessment tool, while most still preferred the more conventional assessment methods, such as on-site speeches and presentations. These results carry pedagogical implicati...

 Abstract—Examinations at the end of a one semester course at a University of Applied Sciences give at best a snap shot of the learning results at a given point of time. Unfortunately, this information is far from sufficient to analyze and evaluate the learning progress during the duration of a particular course. If one intends to continuously improve the course with respect to an optimization of the learning progress, a more detailed analysis is necessary. For this reason, a model has been developed based on the hypothesis that learning progress is mainly governed by two effects: accumulation and segregation. A comparison of predicted examination results with actual ones permits to identify - by means of an optimization process - two parameters representing the respective strengths (weights) of these effects. The values of these two parameters then allow to classify the learning progress within the respective course and to represent a basis for such a continuous course improvement process.

Massive Open Online Courses (MOOCs) have been the focus of online education in recent years. Compared to traditional courses, indirect communication and trans normal number of students in MOOCs bring great difficulties for instructors in knowing how much and how well students have learned. In this paper, a smart visual analysis solution for MOOC data is proposed. The solution contains four views, Demographics Distribution View, Activeness Calendar View, Progress Distribution View and Personal Footprints View. We also integrate data mining results into visualization to help instructors quickly build an insight. Our solution provides a direct, explicit, vivid and interactive way for instructors to know about students' learning progress in multiple scales, which was commented as very helpful in the case study.

With the development of the Internet technology, autonomous learning has become to be norm, however the learners may be lack of to global impression of the knowledge, and cannot find the proper way of learning. Moreover, the learner also cannot find which the suitable point to start learning the suitable. And also cannot arrange learning progress. These problems make it is hard for the beginner to learn. This paper proposed a personal "knowledge map” to help planning the study path. Through testing the learners’ knowledge construction, we can construct the knowledge map for each learner, and then plan a study path which can automatically achieve the goal of knowledge map. According to the plan of the learning path, it is easy for learners to make learning schedule, so as to achieve the purpose of learning.

This paper presents the Learning Scorecard (LS), a platform that enables students to monitor their learning progress in a Higher Education course during the semester, generating the data that will also support the ongoing supervision of the class performance by the course coordinator. The LS uses gamification techniques to increase student engagement with the course. Business Intelligence best practices are also applied to provide an analytical environment for student and faculty to monitor course performance. This paper describes the initial design of the LS, based on a Balanced Scorecard approach, and the prototype version of the platform, currently in use by graduate and undergraduate students in the fall semester of 2016-2017. info:eu-repo/semantics/publishedVersion

We examined a web-based mathematics progress monitoring tool for second graders. The tool monitors the learning progress of two competences, number sense and computation. A total of 414 students from 19 classrooms in Germany were checked every 3 weeks from fall to spring. Correlational analyses indicate that alternate-form reliability was adequate for the chosen interval (.81 < r < .87). Results from latent growth curve modeling (LGCM) identified significant linear increases in students’ scores, and a significant variance in computation slopes was observed. Criterion validity coefficients, comparing the measures with student performance on standardized school achievement tests at the beginning and the end of the school year (DEMAT1+, DEMAT2+), were satisfactory (.59 < r < .76). Students conducted the tests independently, and assessment times were short. Implications for further research and classroom practice are discussed.

This paper presents some empiric results on teaching basic Mandarin as second language to college students using gamification approach. This study shows some evidences that gamification outperform non-gamification teaching method in related to learning concentration, skills, feedback, and immersion. However, gamification method has some drawbacks in related to compliance of quiz difficulty with the level/learning progress, clarity of overall learning goal, and clarity of goal at each level. These strength and drawbacks of gamification design are expected to give valuable information for teachers in delivering Mandarin as second language.

This paper investigates the learning progress of inexperienced agents in competitive game playing social environments. We aim to determine the effect of a knowledgeable opponent on a novice learner. For that purpose, we used synthetic agents whose playing behaviors were developed through diverse reinforcement learning set-ups, such as exploitation-vs-exploration trade-off, learning backup and speed of learning, as opponents, and a self-trained agent. The paper concludes by highlighting the effect of diverse knowledgeable synthetic agents in the learning trajectory of an inexperienced agent in competitive multiagent environments.

We present a computational model of action discovery, which reproduces the functioning bias that infants exhibit during gaze-contingent learning. This first result encourages us to apply the model to ongoing gaze-contingent learning tasks in which aspects like memory, control, and learning progress are studied. The model allows us to explore effects of learning speed, habituation, and prior knowledge on simulated behavior. By comparing simulations with empirical data, we can then devise mechanistic explanations about the observed behavior on the level of groups of subject as well as the individual. Additionally, the model allows us to develop testable predictions about gaze behavior in experimental scenarios that have not been realized yet. We believe that by capturing the essence of behavioral experiments, the model may help to bridge the gap between neuronal processes and human behavior.

Over the last few decades, there has been a growing interest in technology assisted multimedia learning. The purpose of this study was to develop an online multimedia learning system, TEDQuiz, for English language learners to practice their listening skills. The system is comprised of an easy-to-use browser extension and a personalized online learning management platform. This can help learners to manage listening materials by automatically generating multiple -- choice questions, synchronously connecting to friends in a social network website and personally managing their learning progress and word bank. The use of the system involves six steps: skimming, asking questions, listening, answering questions, linking and reviewing. Taking TED talks as an example, users can skim the website, have guiding questions, listen to talks, answer the questions, link to a social network website, and review their profile on the TEDQuiz website. The experimental results demonstrate that students with the TEDQuiz system spent more time on video watching. In the questionnaire, students had a positive view of the functions of the TEDQuiz system. They thought it is useful and helpful, and they were willing to use it in the future. We also found that the usefulness factor was statistically significant in predicting the future usage of TEDQuiz.

While online learning is already a part of university education and didactics, not all students have the necessary self-regulation competency to really learn on their own efficiently and effectively. In classroom a teacher can take over a moderating part, set intermediate goals and give feedback to one's progress, but participants of online learning courses (e.g. in blended scenarios or Massive Open Online Courses (MOOCs)) face a higher demand of self-regulation competency. This paper presents a course and content independent assistant, PeerLA, which assists in improving self-regulation competency. PeerLA allows setting of long-term goals, breakdown into intermediate goals and keeps track of knowledge increase or time needed. A graphical feedback allows comparison of existing and aimed level of knowledge or time investments. PeerLA adds peer comparison to the visualization charts for social frame of reference. This comparison is course-wide or only with similar learners (close in goals and knowledge levels). PeerLA is implemented as a Learning Management System (LMS) plugin to support learning progress in mixed formal and informal learning scenarios. PeerLA was evaluated with 83 students in an online mathematics preparation course over four weeks. Results indicate the benefits of such a self-regulation assistance, especially for university freshmen.

A mock examination is an exam that does not count for credit. It is taken before an official examination and gives students the opportunity to practice for the later, important exam. The mock examination provides students with information on their actual learning progress and gives them in this way the opportunity to fill knowledge gaps. An electronic mock examination is the online variant usually taken unsupervised in the students' own time. In this work we present the setup of an electronic mock exam in the course "Basics of Computer Science". We discuss question types other than multiple-choice that allow for fully automated marking. Furthermore, we present an in-depth analysis of the results of the mock exam. These results demonstrate that students consider the electronic mock exam as valuable help for exam preparation. Half of the students take the mock exam even more than once and use different strategies to make the most out of it. On the other hand, lecturers get invaluable feedback from the results of the mock exam. This feedback covers all learning objectives of the entire course, comes at the right time, and is ideally suited for planning the contents of a recapitulation lecture.