Learning Technologies and Systems

Augmented Reality (AR) has the potential to enhance students’ learning experiences. To deploy AR as educational tool, access to the requisite infrastructure and readiness of teachers are basic conditions to be met. The lack thereof is the digital divide that can undermine the opportunity and ability to use AR educational applications (AREAs) for students and teachers. Such issues were identified in a survey with a sample of European teachers on their educational usage of AR. We aimed to study the digital divide more globally by running the same survey with teachers in South America. We collected 123 valid responses from ten South American countries. Comparing the results of the two datasets showed that the teachers sampled from both continents were facing the challenges of the digital divide to utilise AREAs. We discussed how these challenges could be addressed to allow both, teachers and students, to benefit from AR as educational tool.

Motivating student learning and enhancing student learning performance can be done by gamifying existing learning systems via adding gaming elements to the systems, or by developing dedicated games for learning purposes. However, it is both costly and challenging to develop such systems, since it requires to fill the gap in cognition and knowledge between educators and game designers before they can properly transfer learning theories to game mechanics and elements for gamification or serious game implementation. This paper proposes to gamify experiential learning theory by mapping it to the positive and negative feedback mechanisms of the internal economy of game machinations. Hence, developing serious games becomes easier, since the effort of getting mutual understanding between educators and game designers can be effectively reduced. Our study assesses the proposed model by obtaining the experts’ (game designers/educators) perceptions of the model mapping, and its usefulness and usability, via an online questionnaire. A total of 23 participants answered the questionnaire by stating that our model was useful and could suitably mapped the learning theory to game design. We also present a field-based learning game as an application of our proposed model.

Great learning opportunities are provided through MOOCs. However, MOOCs provide a number of challenges for students. Many students find it difficult to successfully finish MOOCs due to a variety of factors, including feelings of loneliness, a lack of support, and a lack of feedback. Additionally, the instructors of these courses are highly concerned about this situation and want to reduce these difficulties for their students. Due to the large number of students registered in these courses, this is not a simple task. To help both instructors and students, we created edX-LIMS, a learning analytics (LA) system that allows MOOC instructors to monitor the progress of their students and carry out an intervention strategy in their students’ learning thanks to a Web-based Instructor Dashboard. Furthermore, this LA system provides MOOC students with detailed feedback on their course performance as well as advice on how to improve it thanks to Web-based Learner Dashboards. This LA system have been used for more than two year in a MOOC at edX. During this period the Dashboards supported by the system have been improved, and as a result, MOOC students now appreciate the fact that they feel guided, engagement and motivated to complete the course, among other feelings. MOOC instructor have improved their student monitoring tasks and are better able to identify students who need assistance. Moreover thanks to the services that the intervention strategy supported by the LA system offer to them, now students and instructors feel that are connected.

The rapid emergence of knowledge graph (KG) research opens the opportunity for revolutionary educational applications. Most studies in this area use KGs as peripheral sources of educational materials rather than a primary tool for Instructional Design. Considerable effort is required to maintain the alignment between KGs and other elements of Instructional Design practice, such as syllabus, course plans, student learning objectives, and teaching outcome evaluation. To bridge such a gap, we present a novel framework named Knowledge Graph Reciprocal Instructional Design (KGRID), which employs KGs as an instructional design tool to organize learning units and instructional data. Viewing the two aspects as a unified ensemble achieves interactive and consistent course plan editing through manipulations of KGs. The included interactive course schedule editing tool distinguishes our framework from other timetabling approaches that only handle the initialization task. Managing instructional data in KG format is indispensable in establishing a foundation of mass instructional data collection for KG research. We envision a collaboration between the two disciplines. With these crucial functionalities and aspirations, KGRID outperforms the practice of replacing Instructional Design tables with concept maps. We present the system architecture, data flow, visualization, and algorithmic aspect of the editing tool.

In this paper, we present research aiming to understand usage behaviors of elementary school teachers when using a scenario-based platform dedicated to the teaching of Computational Thinking concepts. In order to understand their behavior, we have defined a two-step analysis process to identify usage patterns on the platform. The first step consists of performing a clustering algorithm and the second one consists of characterizing each obtained cluster by a pattern mining algorithm. On the one hand, the k-means algorithm allows us to identify four significant clusters of teachers using the platform. On the other hand, the sequential pattern mining algorithm allows to characterize precisely the usage behaviors of each cluster. Thus, this two-step analysis process was evaluated with teachers data. We carried out an experimental study during two workshops with 29 teachers. Teachers interaction’s data were collected from ScenoClasse, resulting in a data-set of 498 traces. As a result, we identify different usage behaviors for teachers while using ScenoClasse. Based on their behaviors, teachers can be adapters, explorers, viewers, or beginners.

This article describes the Compete! educational game, developed within the European Erasmus + framework, which aims to teach soft skills to higher education students to increase their employability. A participatory learning methodology based on a gamification tool has been used for this purpose. The game presents a series of scenarios that describe social sustainability problems and require applying of soft skills identified as key competences in a field study across different European countries. These competences are: creative problem solving, effective communication, stress management, and teamwork. On completion of each game scenario and the game itself, students receive an evaluation of both their soft skills and the strategic and operational decisions they have made. In the evaluation of these decisions, both economic and sustainability aspects of the decision are assessed. The teacher can then address the competences and sustainability issues using the different scenarios of the game, thus creating higher motivation and deeper understanding amongst the students. This hybrid learning methodology incorporates digital tools for the cross-curricular teaching-learning of sustainability and soft skills. It is therefore a methodology that promotes the SDGs.

English is the most spoken language in the world. Despite this, only 1.35 billion people worldwide use English as their first or second language of communication. English is the world’s main language, 83% of the world’s population do not speak English. As a result, most people must spend considerable money and time to learn English. Continuous practice ensures that the language develops as often as possible. Developed, Pocket English Master (PEM) mobile app assumes the role of a full-fledged English teacher to solve the above problems. This application entails conducting an early assessment of the student’s present English competence and directing him or her to the appropriate class. Using artificial intelligence, a curriculum is created that matches the knowledge level of the students. The application enables students to complete learning activities at their convenience by evaluating the student’s learning style using artificial intelligence and making the student’s daily activities efficient and aesthetically pleasing using reinforcement learning and algorithms. This includes an artificial intelligence component to improve students’ ability to speak and read English as well as their ability to engage in natural relationships. Also, the app incorporates augmented reality, real-time handwritten text scanning, and real-time grammar and spelling error correction.

The automated correction of assignments is a task that received large support from artificial intelligence. In previous research, we approached the problem of automatically providing a grade and feedback to students in solving data science exercises, that resulted in the development of the rDSA tool. In this paper, we discuss the first steps towards the development of an adaptive system – based on the rDSA tool – supporting the students’ formative assessment activities. In particular, we present the context of use, the requirements – elicited through a study with a small cohort of students, the models enabling adaptation, and the user interface. Finally, we evaluated the user interface through a further study that involved both qualitative and quantitative measures.

Videogames have proven their usefulness in numerous fields such as education and training. This kind of videogames, usually called serious games, can also be powerful classroom tools for raising awareness about complex societal issues (e.g., gender discrimination, sex stereotypes). In this paper we describe the process followed to develop a video game to raise awareness about gender discrimination. The videogame provide students with a first-hand experience of what is feeling discrimination. With this shared experience, teachers can open a discussion on the topic in a post videogame session. We describe the first formative evaluation of the videogame with 26 users, showing some initial positive results and initial feedback to continue working to improve the videogame. Once validated the game will be accessible as open code to be freely used and even adapt and localize it to different cultures. This work will help other researchers who want to develop serious games to address social problems and also serves as an example of the usefulness of video games in addressing complex problems such as discrimination.

The context of teacher is indescribable without considering the multiple overlapping contextual situations. Teacher Context Ontology (TCO) presents a unified representation of data of these contexts. This ontology provides a relatively high number of features to consider for each context. These features result in a computational overhead during data processing in context-aware recommender systems. Therefore, the most relevant features must be favored over others without losing any potential ones using a feature selection approach. The existing approaches provide struggling results with high number of contextual features. In this paper, a new contextual ontology-based feature selection approach is introduced. This approach finds similar contexts for each insertion of new teacher using the ontology representation. Also, it selects relevant features from multiple contexts of a teacher according to their corresponding importance using a variance-based selection approach. This approach is novel in terms of representation, selection, and deriving implicit relationships for features in the multiple contexts of a teacher.

Over the years, several systematic literature reviews have been published reporting advances in tools and techniques for automated assessment in Computer Science. However, there is not yet a major bibliometric study that examines the relationships and influence of publications, authors, and journals to make these research trends visible. This paper presents a bibliometric study of automated assessment of programming exercises, including a descriptive analysis using various bibliometric measures and data visualizations. The data was collected from the Web of Science Core Collection. The obtained results allow us to identify the most influential authors and their affiliations, monitor the evolution of publications and citations, establish relationships between emerging themes in publications, discover research trends, and more. This paper provides a deeper knowledge of the literature and facilitates future researchers to start in this field.

Developing Serious Games (SG) is a complicated and expensive process, involving experts in game development and the use of specific tools. This is one of the barriers in the adoption of SGs. Few SG-specific authoring tools were proposed in the literature but lacking either in giving educators control in all aspects of the creation, or varied activities in the end product. To further reduce the process complexity and dependence on game experts, we propose an authoring tool specifically aimed for educators to create SGs themselves. This paper reports findings from a summative usability study conducted on a high-fidelity prototype of the tool with educators, who perceived the usability of the tool was high.

Motivated by the successful applications of commonsense knowledge graphs (KGs) and encyclopedia KGs, many KG-based applications have been developed in education, such as course content visualization and learning path/material recommendations. While KGs for education are often constructed manually, attempts have been made to leverage machine learning algorithms to extract triples from teaching materials. However, education-related KGs learned by existing algorithms contain significant amounts of redundancy and noise. It is because the entities and relations in teaching materials are often instructional, abstract, and implicit, while textbooks often contain detailed explanations, examples, and illustrations. Off-the-shelf KG construction algorithms are designed for concrete entities. To this end, we propose an effective framework to construct low-redundant and high-accuracy KGs for education. First, we design an ontology that is tailored for education. By choosing related Wikidata items, we construct an instructional entity set. We avoid using traditional methods such as named-entity recognition to extract entities from textbooks, aiming to reduce redundancy. Then, we add subtopic relations among our selected instructional entities based on the corresponding hierarchy in Wikidata, and form a backbone. Second, we design a machine reading comprehension model with pre-defined questions to extract other types of relations, such as equivalent to, applied to, and inventor of. Third, we apply active KG error detection to further refine the KG with minimal human effort. In the experiments, we take the artificial intelligence domain as an example and demonstrate the effectiveness of the proposed framework. Our KG achieves an accuracy of around 80% scored by domain experts.

Discussion forums on MOOCs are developing as a major tool for communication between learners and instructors, generating large amounts of posts, exchanged as unstructured text content. Thus, it is a major challenge for instructors to find (and respond to) urgent posts, amongst the vast amount of posts. Learners also may inadvertently pose posts that may seem more urgent than they are. However, the current literature lacks research on analysing posts from an urgency language perspective, i.e., extracting the language used for urgent expression. This paper explores for the first time the urgent language that learners use to express their need for immediate intervention, via an automatised approach. It describes our analysis of 5181 text posts from a course from the Stanford MOOCPosts dataset, selected for its good representation of urgent posts. We use topic modelling, here, via the widely used latent Dirichlet allocation (LDA). Moreover, we demonstrate a correlation between specific topics and urgent posts. Also, we show that most urgent posts start new threads. Additionally, constructing a visual interface for instructors or learners may support them understand the urgent language and improve intervention.

Sharing of educational resources is an important aspect supporting the exchange of knowledge, skills and experience between the pedagogical specialists that be-come of vital importance with the forced distance learning during Covid-19 pandemic. The paper presents the design and development of a web based digital content repository that is used as national teachers’ digital library in Bulgaria. The digital repository is aimed at publishing and sharing authored educational, didactic and methodological materials created by primary and secondary teachers. The platform allows sharing different teaching materials as presentations, video lessons, films, exercises, tests, training programs, innovative teaching methods, entertaining pedagogy as well as research and student’s work. The author's materials can be used for training, self-training and testing and are stored by grades, by thematic areas and by material type. All the materials are audited for correspondence to the state educational standards and the relevant curricula. The developed web based digital content repository serves as a national digital library for e-learning materials used in primary and secondary schools in Bulgaria as well as in Bulgarian schools abroad.

Measuring the effectiveness of teaching is a complex and multifaceted process which requires understanding both the context and assessment goal. Based on an OECD report [7] the quality of the educational system relies heavily on the systematic assessment and development of teachers’ competence while focusing on reflection. Although teachers’ digital competence has been a central discussion point for the past decade, researchers are still struggling to understand what are the most effective feedback solutions to support teachers’ digital competence development. The following paper gives and overview of the results of a design study which focused on understanding the digital competence self-assessment feedback loop. Based on the design study we proposed and evaluated a feedback loop prototype which supports teacher’s digital competence and meaningful professional development.

In recent years, there has been an exponential growth in the demand for distance learning. In addition, the pandemic from COVID-19 has radically changed the techniques but also the teaching/learning methodologies. In such a picture, Massive Online Open Courses (MOOCs), are increasingly emerging on the Web. Due to the big number of students, in MOOCs the traditional monitoring and instructional interventions, performed by the teacher on individual learners, are of difficult, if not impossible, application. An Artificial Intelligence approach, based on Virtual Conversational Agents or Intelligent Chatbots, can help overcoming such difficulties. In this paper we present a system, at its early stage of development and based mainly on a deep learning model, able, at different levels, to suggest didactic material to students in a query/answer modality. It is able to answer students’ questions by proposing didactic material taken both from a specific knowledge domain or from Wikipedia.We investigate the potential of such an early stage implementation, through several case studies. We also present a qualitative evaluation, based on the case studies findings, which we think is encouraging towards the development and field experimentation of a whole system.

The open and online nature of Massive Online Open Courses (MOOC) can be a way to improve the success and efficiency of formal educational paths in Higher Education because the MOOC model provides a flexible approach that promotes different ways of learning. However, teachers must be trained and confident so that they can fully explore the methodologies, tools and resources that result from the integration of MOOCs into a formal curricula. This article reports the results of a MOOC-based training program, created and implemented in the scope of a European project, aimed at enabling teachers from Higher Education Institutions (HEIs) to develop and deliver MOOCs. The training clearly contributed to increasing the teachers’ competences related to those objectives and was a relevant component for their professional development namely in terms of adopting innovating teaching practices.

The academic success of students can be improved by an understanding of the academic domain they are navigating. As such, they may benefit from gaining valuable perspective into the shape of their chosen field via an enhanced visual aid. We discuss K-Cube VR, a work-in-progress academic domain browser, which provides visualization of such information through knowledge graphs in virtual reality. We then present the theoretical motivations of such a project, proposing in the process new principles for learning in the metaverse. These principles are an attempt to integrate current educational trends, immersive learning, and visual literacy, within a constructivist pedagogical framework. It is hoped that the proposed principles and our implementation via K-Cube VR can inspire future metaverse learning environments.

Serious Games provide an opportunity to address social issues in an interactive environment particularly appealing for school-aged children. Gender stereotypes are one of the many remaining gender-related issues in current society. Stereotypes appear at early ages and are particularly prevalent in certain cultures and countries. This paper presents an early acceptance evaluation of Kiddo, a serious game to address gender stereotypes in Mexico. The game has been designed to address four of the main stereotypes still present in children in this country, and it is intended to be used in classes by teachers to start a discussion about gender stereotypes. The evaluation has been carried out with a prototype of the game containing one of the game chapters and a sample including teachers and gender experts. The goal is to verify both the usability of the game as well as its acceptance for some of the target users that will later apply the game in their classes. Results of both usability and acceptance questionnaires have provided a useful insight into the strengths and areas of improvement for the game, and they are being incorporated into the new version of Kiddo.

This intervention seeks to improve the teaching of the Technology subject in the 1st and 2nd courses of secondary education, introducing in the classroom aspects related to the Maker Movement in education through Maker Education, a learning approach that focuses on practical learning through projects and is related to STEAM education. The main objective of this work has been to generate a significant experience in students that helps them to awake interest in scientific-technological areas. This has been worked through a project based on Maker Education in which the students, in groups, have developed a Chained Effect project, working on skills such as creativity, problem solving, teamwork and collaboration, and having the opportunity to put into practice the knowledge acquired. During this experience, a visit to a Makerspace Las Cocinas belonging to the School of Industrial and Civil Engineering of University of Las Palmas de Gran Canaria was also made, aiming at including new spaces in the teaching-learning process. This space was used to teach them the process of designing and manufacturing of customized parts for each student, bringing students closer to the world of engineering, design and manufacturing through meaningful learning, with the goal of impacting favourably on them.

The advantages of mobile learning (m-learning) in English education have been widely described in previous research; however, there is little evidence of its effects on student outcomes. To fill this research gap, we conducted a meta-analysis of 54 empirical studies to measure its impact on student achievement. In addition, we estimated the moderating effects of the pedagogical approach, the learning environment, the education level, the control treatment, and the mobile device. The results indicate that m-learning has a large impact $$(g=0.94)$$ on students’ achievement. This effect is influenced by the pedagogical approach, the education level, and the control treatment, but not by the learning environment or the mobile device. Finally, we explain the nature of these results in light of learning theories.

This study proposes the use of virtual tours as an interactive tool in engineering laboratories in order to increase the student’s motivation and academic performance. The experimentation space corresponds to the integrated manufacturing systems laboratory on campus, which has specialized machinery, such as CNC equipment, industrial robots, and material handling and storage systems. In the first stage, students will not only be able to visit the laboratory remotely but also learn about the components of a manufacturing system. In this way, they will be able to identify the elements and their interaction while learning about specific equipment features. As part of the Industry 4.0 strategy, the virtualization of systems and analysis tools is a global trend. Through this project, students are expected to interact with cutting-edge technology and face real situations that will allow them to develop disciplinary and transversal competencies.

Data is an important prerequisite for research on smart education. The development of sensing technology and flexible wearable devices have greatly improved the ability of data collection, data analysis, and data application. In this review, based on the analysis of the features and functions of sensing technology, and the history of wearable devices, we analyze the trajectory of sensing data approaches to flexible wearable devices for smart education. Likewise, we summarize recent advances in wearable devices in different educational situations, with particular emphasis on the methods and applications of wearable devices in smart education based on sensing data. The challenges and opportunities relating to wearable devices based on sensing data for smart education are also discussed.

Verbs are not only the core of sentences, but also the focus of vocabulary research and teaching. This paper takes a commonly used verb xiàn ‘trap’ as an example to explore the use of verbs. This word is listed in The Chinese Proficiency Grading Standards for International Chinese Language Education, which has been newly implemented worldwide since 2021. This study has analyzed the syntactic use of xiàn ‘trap’ from the aspects of syntactic functions and intensity of syntactic collocations, as well as the semantic use of xiàn ‘trap’ from the aspect of its semantic roles. It combines quantitative statistics and qualitative analysis to explore the syntactic and semantic use, which not only deepens the linguistic research of verbs, but also provides reference for verb teaching in language education and the compilation of Chinese learners’ dictionaries.

Vocational education and training in Germany are distinguished by the separation of training groups of different learning levels. In chef training, theoretical teaching content is mainly conveyed using lectures and textbooks. The abstract presentations and texts are not suitable for inclusive education. By pursuing a design science research process, a digital-analog learning application was developed for trainees with and without mental impairment. The tangible prototype for teaching meat cutting from pork was evaluated using 7 participants (trainee chef, trainee kitchen assistants and employees in the kitchen of a sheltered workshop) in an inclusive teaching setting with a mixed-method approach. The usability was evaluated with a User Experience Questionnaire, the results of which were positive. The learning outcomes using the 3D model were surveyed by means of a qualitative questionnaire and showed a high level of potential among all target groups. Two unstructured classroom observations documented the social interaction of the trainees. The learning application facilitates learning and promotes collaborative work.

There is an urgent need to advance theoretical research and broaden academic conversation in relevant domains in order to undertake strategic initiatives for the digitalization of education. The topics of metaverse and educational ecological reconstruction, artificial intelligence (AI) and future educational development, big data and educational governance, and future educational research paradigm change are thoroughly discussed through interviews with 16 experts in the field of educational information technology. The study found that: (1) ‘Metaverse’ should be substituted to the development of education, it brings new problems to education, and also put forward new requirements for modern education; (2) Artificial intelligence is an all-round challenge to education, which has a significant impact on talent training, both in teaching and learning, as well as the restructuring of the entire education system; (3) Under the background of artificial intelligence, the future education will mainly lead to changes in educational values and objectives, and changes in the means of educational scenarios will also lead to changes in traditional educational forms; (4) The duplexing empowerment between education research and new technology; (5) In the context of new technologies, such as AI and the Metaverse, science fiction could be used as a new method for educational research..

Children and adolescents with special health care needs require specialized pediatric care and, possibly some period of hospitalization in a tertiary care center with high complexity. This work aims to help the treatment of this children identifying well-being factors during the hospitalization and home care convalescence through game-based co-creation methods. The explorative study follows the qualitative and multimethod approach. In this paper, it is described the co-creation procedure and the methodological proposal for children and adolescents.

Nowadays, overwhelmed as people are by the amount of information available, it becomes more and more difficult to build an adequate cognitive process of knowledge building and discovery, on any one knowledge domain. Therefore, having knowledge-building tools at disposition, especially in the age of schooling, is of great importance. Knowledge building occurs by linking new concepts to already learned ones, thus connecting concepts together by means of semantic links representing their relationship. To accomplish this task, most of learners use Concept Maps, that is graphic tools, particularly suitable, to organize, represent and share knowledge. In fact, a Concept Map can explicitly express the knowledge of a person or group, about a given domain of interest: from primary school to university, and to professional/vocational training, Concept Maps can stimulate and unveil the occurrence of the so-called meaningful learning, according to the Ausubel’s learning theory. In this paper we investigate the use of a deep learning-based architecture, called TransH, designed for Knowledge Graph Embedding, to support the process of Concept Maps building. This approach has not been yet investigated for this particular educational task. Some preliminary case studies are discussed, confirming the potential of this approach.

The use of Machine Learning (ML) to resolve complex tasks has become popular in several contexts. While these approaches are very effective and have many related benefits, they are still very tricky for the general audience. In this sense, expert knowledge is crucial to apply ML algorithms properly and to avoid potential issues. However, in some situations, it is not possible to rely on experts to guide the development of ML pipelines. To tackle this issue, we present an approach to provide customized heuristics and recommendations through a graphical platform to build ML pipelines, namely KoopaML, focused on the medical domain. With this approach, we aim not only at providing an easy way to apply ML for non-expert users, but also at providing a learning experience for them to understand how these methods work.

Grammar instruction has long been an important task in foreign language teaching to foster students’ grammatical competence. However, in terms of sentence grammar, the current grammar instruction gives preference to teaching the usage of certain syntactic patterns, while ignoring the clause-complex-level grammar that governs clause combing. As a result, students may encounter difficulties when organizing several clauses into a clause complex, and thus fail to produce long sentence of high quality. To unveil the issue, this study proposed a classification system for grammatical clause-combining strategies under the Clause Complex Theory, and explored how Chinese EFL learners master four subtypes of grammatical strategies based on the CLEC corpus. The error analysis reveals that unfamiliarity of the strategies and the negative transfer of mother language are two main reasons for the misuses of grammatical clause-combining strategies. Hence, a systematic grammar instruction on the clause complex level is called for, so as to improve Chinese EFL learners’ ability to write structurally-correct clause complexes.

Business English (BE) is widely taught around the world, especially in China. In order to improve the teaching of BE in China, the fundamental lexical and syntactical features of BE writing by undergraduate students are investigated under the theoretical framework of sociolinguistics. First, students BE writing was collected to construct a writing corpus. Then, the corpus was quantitatively analyzed with self-coded Python programs. At last, the lexical and syntactical features were also manually examined to ensure the accuracy of the research. The results of this study find that the linguistic features of BE writing by Chinese undergraduate students are in concordance with sociolinguistic theories, especially from a developing perspective. This finding may be helpful in BE writing teaching and curricula design in China.

Learner portrait is a term derived from “user portrait” in business marketing. Many educational apps try to model a portrait for individual users, so that targeted course recommendations can be provided for each learner. With the wide adoption of blended learning, learning apps has become an indispensable part of college education. Scholars have been working on more accurate portrait constructing, so that more personalized learning plans and better content recommendations can be achieved. Compared to studies on portrait modeling, much less research has been done in the role learner portrait plays for learners and teachers—the two key possible users of learner portrait. Even less literature can be found in the use of portraits for intermediate and advanced language teaching and learning, which is more skill-based and less easy to provide a clear knowledge map. This research focuses on the impact of portrait feedback provided by a listening and speaking app used in a blended college level English as a Foreign Language (EFL) course. The app generates group portraits for teachers, which divide learners into 9 categories based on a profile of learners’ time on app and academic performance. Individual portraits that summarize their performance in the app are also provided for individual learners. Through platform data analysis, questionnaire surveys and in-depth interviews, the study finds that: 1) Teachers’ interventional guidance based on group portraits effectively promotes student learning; 2) Students adjust learning plans based on portrait feedbacks; 3) Students hope that the platform can provide more refined portraits with more detailed analysis of learning behavior data.

Motivation, both intrinsic and extrinsic, are fundamental in language learning. This study aims to conceptualize and construct a gamified flipped model for English as Second Language (ESL) classes for secondary schoolers to explore whether they can be motivated to learn English, and to examine participants’ attitudes toward the model. The gamified flipped model, which is a dynamic combination of the flipped classroom with game elements, is to foster ubiquitous learning by students both in and out of class. The author reviewed relevant literature on ESL, gamification, and flipped classrooms, and designed a model to turn the traditional ESL class into a real-person boardgame. Preliminary feedback shows that participants are motivated to increase their out-of-class learning thanks to the new model. The study can shed light on the pedagogy of ESL.

While previous studies have focused on the outcomes of using dubbing apps to foster learner’s development of English news broadcasting ability, this study, grounded in cognitive apprenticeship (CA), is an investigation of the learning processes, which included modeling, coaching, scaffolding, articulation, reflection, and exploration, involved in the use of a dubbing approach. Participants included 216 college students at a university in the mainland China. Data included deep speaking pre-and post-test scores, initial and final dubbing videos, learning logs, and reflective essays. The results showed that after engaging in video dubbing the students improved their English-speaking and English news broadcasting ability, in terms of accuracy and fluency. Coaching and modeling were ranked as the two most useful processes which supported students as they improved their English-news broadcasting ability through repeatedly listening, echoing, and imitating. Implications and limitations of the study are discussed.

Even though computers and mobile devices are widely applied to language teaching and learning, few attempts have been made to explore the learning engagement of higher vocational college students in MALL environment. This study set out to investigate learning engagement of higher vocational college students in MALL by using validated learning engagement scale. A total of 339 students were enrolled in the investigation. The data were analyzed from three dimensions: behavioral engagement, emotional engagement and cognitive engagement. The results reveal that higher vocational college students’ learning engagement in MALL environment is at a moderately high level. Additionally we found that there is no significant difference between male and female college students in English learning engagement. And there is no significant profession difference on emotional and cognitive engagement, but a significant profession difference exists on behavioral engagement in MALL environment.

This paper focused on students’ learning experiences in a flipped data science class integrated with peer instruction and just-in-time teaching. University students in Hong Kong participated in the research during the pandemic. Students’ perceptions of the flipped learning mode were investigated by a 5-point Likert scale questionnaire. According to the results, most students felt they enjoyed learning with the flipped mode since it allowed them to learn flexibly and independently. The course materials (i.e., instructional videos, lecture notes, and in-class exercises) were well-designed, and students perceived the materials were useful and user-friendly. As suggested by the students, more time should be given to the pre-class learning activities. Based on the suggestions, the researchers provide practical implications on improving teaching in the flipped science class. This study demonstrates how to flip a data science class and proves its value on students’ learning, providing implications for using flipped learning during the pandemic period.

Providing feedback in a LMOOC context is challenging. Video feedback has the potential to serve online language learners on a large scale, as they can download, study, revise these feedback videos repeatedly and hopefully transfer their learning to complete future tasks. Vicarious learning feedback videos have not been investigated extensively, as most previous studies focused on the extent to which feedback receivers can learn from their own mistakes and feedback directly addressed to their work. The opportunities to learn from others’ mistakes and feedback on others’ work have more significant potential to serve an extensive online learning community. This study filled the gap by investigating and comparing learners’ preferences and the use of two types of vicarious learning feedback videos as self-directed learning resources in LMOOCs, i.e., teacher and peer feedback videos.This study collected both quantitative and qualitative data from 16 s language learners. Quantitative data mainly came from the dashboard of a learning management system, including frequency (the frequency of each video being played) and engagement level (the proportion of each feedback video being watched). Qualitative data referred to participants’ reflections on their learning experiences with these videos. The results suggested that 1) teacher feedback videos were watched more frequently than others as learners regarded them as their desired performance and ultimate learning goals. They preferred teachers’ explanations of the expected learning outcomes and elaborations of marking rubrics in the videos; and 2) the online learners watched a much higher percentage of the content in peer feedback videos, which indicated a higher engagement level with the peer feedback videos than teacher feedback videos. Interview data suggested that it was in the peer feedback videos that online students carried out more peer evaluation, self-reflection, and assessment practices.

This paper provides a review of empirical studies on the adoption of a blended learning approach in teaching English as a second/foreign languages since 2021. The review contributed to the topic by documenting and synthesizing latest empirical evidence. To select articles, two international education databases were included: SCOPUS and Web of Science. After excluding theoretical paper, literature review and commentaries, 9 articles remained. The in-depth analysis revealed that the current main topics of these studies included the student perception, teacher perception, enhancing learners’ specific language skills as well as students’ language learning autonomy. In addition, the blended learning designs in these studies were various, combining different instructional methods, multiple delivery media, and pedagogical approaches. It can be concluded that with the significant effectiveness of blended learning approach in English study, numerous context-specific challenges were existed. In addition, there is a need for more related studies to focus on the K-12 setting as well as the long-term influence of blended learning approach in learners’ English proficiency and abilities.

With the advancement of online learning technologies, translation learning, teaching, and learning motivation have been influenced and even reshaped. So, there is a need for empirical investigations on the motivation in learning translation online. Under the framework of the L2 Motivation Self System, this study uses exploratory factor analysis and structural equation model to analyze 150 college students’ motivation for online translation learning and identifies six motivation factors: IL2S Factor, Escape/Stimulation Factor, Social Contact Factor, Social Service Factor, L2LE Factor, and OL2S Factor. The research findings show that motivation in online translation learning is not instrumental, and the Ideal L2 Self is the most significant and dominant motivation in online translation learning.

Translation for children inevitably involves editing to the text as children have their own cognitive characteristics and restrictions. In this regard, this study takes translation of children’s literature as a kind of transediting and probes into the principles and methods of transediting children’s literary works. Taking the classic children’s literature The Wizard of Oz and its Bookworm bilingual version as a case for analysis, this study finds that transediting of children’s literature involves considerations from three dimensions—narrative, language, and culture. To tailor to children’s cognitive characteristics, transeditors usually make adaptions to the original text. Such measures include deleting branching plots and extracting the main storyline, using basic vocabulary and simple sentence structures, and transforming foreign cultural elements into familiar ones. The study also points out that technology-empowered children’s literary books have shown their advantages in enhancing children’s reading experience and in giving full play to the educational significance.

The research aims to prove knowledge network acceptability in/between Chinese and English translation learning by interviewing 82 junior EFL undergraduates who have used a computer-based platform for one semester. With the assistance of Likert Scale and statistical tools, it reveals that students can widely accept the concept, translation knowledge network, and this computer-based translation learning strategy, and they even consider this strategy more efficient than traditional offline teaching-learning activity. Based upon exploration of translation learning strategy on the platform, experiment design and analysis of experiment data, the study finds how the knowledge network acceptability can enrich students’ translation learning strategies, learning resources management strategies.

The current translation teaching turns out to be poor in efficiency. Many students lack practical ability and cannot sufficiently deal with translation tasks in reality. How to motivate their study initiative and improve their translation ability is the question we are interested in. This study tries to make full use of the computer technology and advance the traditional teaching mode with the aid of the ClinkNotes online platform, designed for translation teaching. In accordance with the characteristics of translation course, we, taking the Task-Based Language Teaching approach, apply this platform and design the classroom teaching in a new way. Students are well encouraged to participate in the study and give prompt responses to the translation task with several rounds of discussion. It finds that the design of this teaching model can have a positive impact on students’ learning efficiency. We hope this study could be enlightening for the future teaching reform.

Communicative rhetoric exhibits the attributes of being transferable and generative, which facilitate the methodological elements of cognitive apprenticeship such as modelling and scaffolding, encompassing a transfer of knowledge-transforming skill-set and strategies to sustain the College English reading-writing teaching. This essay is oriented to explore and visualize the feasible communicative rhetorical paradigms of the teacher and the journeyman situated and recycled in the real-life-issued reading contexts, which are sustainable and transferable to be applied in the English advanced writing. Simultaneously based on the communicative rhetorical modelling in the reading teaching facilitated by the platform of Rain Classroom and the empirical analyzing of target students’ writings submitted to the online intelligent assessing system on pigai.org, this research aims to investigate the validity and effectiveness of the communicative rhetorical modelling on the improvement of students’ advanced writing through interactive reading teaching.

This research aims to provide an empirical study on the digitalized application of a self-developed knowledge-network-based online translation learning platform by conducting the training to junior undergraduates majoring in translation who have used this online platform for a certain period as explained below. In this research, data mining technology is used to explore the internal relationship among translation knowledge nodes and establish translation learning knowledge network, which is considered to be beneficial to translation learning. To prove this, this paper introduces a knowledge-network-based translation training camp based on Textwells, an online translation learning platform, which contains annotated bilingual texts with theory-informed annotations of translation methods. The training camp used the research methodology with a pretest and a posttest to measure training effects. Compared to the pretest, students’ translation performance has been improved substantially in the posttest. Therefore, it is shown that the knowledge-network-based translation learning does help improve students’ translation performance.

The development of informational technology and the proliferation of electronic gadgets make blended learning (BL) possible. In recent years, BL has been widely employed in language teaching. In this paper, the authors give an explicit elaboration on the application of the integration of online platform (rain classroom) with traditional face-to-face (F2F) classroom to medical English teaching under the guidance of POA, which can solve the problem of separation between learning and using of medical English. The thoughtfully designed BL model demonstrates a new way to help teachers to be informed of students’ learning in time, provide materials accordingly for students’ autonomous learning, and offer efficient evaluation on the one hand, and ignite students’ intrinsic enthusiasm for learning, and increase their active involvement on the other hand.

With the help of internet and digital technology, online and blended teaching in institutions of higher learning in China are becoming a reality and the original assessment model is out of date. A scientific, diversified and digital assessment model is badly needed. The paper will present a new model aided by digital platforms applied in HFUT, in which summative assessment accounts for 50% and formative assessment another 50%. This paper focuses on the latter which consists of autonomous listening (10%), oral English (20%), assignment (10%) and class performance (10%). The assessment content, assessment method and means and marking criteria of the above four components are displayed in detail and a questionnaire is conducted so as to reveal its implementation effect. Findings show that students have a relatively high satisfaction with the formative assessment model which proved to be more objective and operable. Nevertheless, respondents’ suggestions taken into account, a slight adjustment and improvement will be made later.

While the popularity of translation technology in professional workflows as well as translation teaching and learning brings translation productivity, it also undermines the development of translators’ own translation abilities to some extent, which happens to be the core competency that distinguishes a good translator in the marketplace, even in the age of artificial intelligence. This paper attempts to bring the focus of translator training back to translation abilities development. It carries out a case study at The Chinese University of Hong Kong, Shenzhen among undergraduate translation students using a knowledge-network-based translator training platform called Textwells for self-learning, putting the acquirement of knowledge and analytical skills in priority, and then conducts an in-depth analysis of students’ work to assess their learning outcomes. The paper may be of interest to translator educators looking to cultivate students’ translation skills and enhance their lifelong learning abilities with the assistance of machines rather than produce assembly line workers purely dependent on machines in the technology era.

Nowadays, the World Wide Web gives access to most of the world’s knowledge, nevertheless, people require competence to interpret and comprehend most of that knowledge. The Lifelong Learning approach introduces a new way of constantly pursuing knowledge consistently. Nevertheless, learners need to test and carry out projects to reinforce the study of theoretical concepts. Mostly in computer science, where they often need to experiment with large amounts of sensitive data to generalize beyond small training samples. However, in the past years, the European government enacted several regulations concerning privacy after catching up with sectors such as AI and Data Mining. The study illustrates a theoretical framework that includes hardware and software characteristics to avoid privacy violations and comply with European privacy regulations. The resulting framework could be applied for learning purposes as well as IoT applications regarding computer vision technologies to ensure correct and compliant communications between different devices.