Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums

This article belongs to the Special Issue E-learning, Digital Learning, and Digital Communication Used for Education Sustainability.Discussion forums are a valuable source of information in educational platforms such as Massive Open Online Courses (MOOCs), as users can exchange opinions or even help other students in an asynchronous way, contributing to the sustainability of MOOCs even with low interaction from the instructor. Therefore, the use of the forum messages to get insights about students’ performance in a course is interesting. This article presents an automatic grading approach that can be used to assess learners through their interactions in the forum. The approach is based on the combination of three dimensions: (1) the quality of the content of the interactions, (2) the impact of the interactions, and (3) the user’s activity in the forum. The evaluation of the approach compares the assessment by experts with the automatic assessment obtaining a high accuracy of 0.8068 and Normalized Root Mean Square Error (NRMSE) of 0.1799, which outperforms previous existing approaches. Future research work can try to improve the automatic grading by the training of the indicators of the approach depending on the MOOCs or the combination with text mining techniques.This research was funded by the FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación, through the Smartlet and H2O Learn Projects under Grants TIN2017-85179-C3-1-R and PID2020-112584RB-C31, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307 and under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation), a project which is co-funded by the European Structural Funds (FSE and FEDER). Partial support has also been received from the European Commission through Erasmus+ Capacity Building in the Field of Higher Education projects, more specifically through projects InnovaT (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), and PROF-XXI (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP)

Analysis of Factors influencing the successful use of Massive Open Online Courses (MOOCs) to prepare Digital Talent

One type of e-learning category is Massive Open Online Courses (MOOCs). MOOCs or MOOC promote the "democratization of education" that allows education to be accessed by everyone from anywhere and anytime. The use of MOOCs gives students access to a wide variety of resources. MOOs enable students to have the sufficient storage capacity to store their materials. MOOCs have the potential to improve digital capabilities in the face of digital transformation. The intention to use MOOC is relatively high, however, in terms of class completion rate and motivation to pass on MOOC is relatively low. These conditions need to be examined to increase the success rate of MOOCs usage. This research develops a model and identifies factors that influence the successful use of MOOCs to prepare digital talent. The approach is a mixed method that collects quantitative data using an online questionnaire and qualitative data via interviews. The researcher took data from 91 samples and eight informants for interviews. In the study results, 6 out of 12 hypotheses are accepted in this study. The factors that influence a person in completing MOOC either directly or indirectly include Performance expectancy, willingness to earn certificates, MOOC quality, and Intrinsic motivation. This research also produces recommendations that can be used as consideration for parties related to MOOC

A reception study of machine translated subtitles for MOOCs

As MOOCs (Massive Open Online Courses) grow rapidly around the world, the language barrier is becoming a serious issue. Removing this obstacle by creating translated subtitles is an indispensable part of developing MOOCs and improving accessibility. Given the large quantity of MOOCs available worldwide and the considerable demand for them, machine translation (MT) appears to offer an alternative or complementary translation solution, thus providing the motivation for this research. The main goal of this research is to test the impact machine translated subtitles have on Chinese viewers’ reception of MOOC content. More specifically, the author is interested in whether there is any difference between viewers’ reception of raw machine translated subtitles as opposed to fully post-edited machine translated subtitles and human translated subtitles. Reception is operationalized by adapting Gambier's (2007) model, which divides ‘reception’ into ‘the three Rs’: (i) response, (ii) reaction and (iii) repercussion. Response refers to the initial physical response of a viewer to an audio-visual stimulus, in this case the subtitle and the rest of the image. Reaction involves the cognitive follow-on from initial response, and is linked to how much effort is involved in processing the subtitling stimulus and what is understood by the viewer. Repercussion refers to attitudinal and sociocultural dimensions of AVT consumption. The research contains a pilot study and a main experiment. Mixed methods of eye-tracking, questionnaires, translation quality assessment and frequency analysis were adopted. Over 60 native Chinese speakers were recruited as participants for this research. They were divided into three groups, those who read subtitles created by raw MT, post-edited MT (PE) and human translation (HT). Results show that most participants had a positive attitude towards the subtitles regardless of their type. Participants who were offered PE subtitles scored the best overall on the selected reception metrics. Participants who were offered HT subtitles performed the worst in some of the selected reception metrics

NANO-MOOCs to train university professors in digital competences

[EN] Rapid changes in technology force Higher Education Institutions (HEIs) to generate policies and permanent digital adaptations in their exercise of forming professionals through university professors. HEIs -in their permanent desire to qualify teaching faculty and graduate high-level professionals- develop continuous training events to strengthen and update techno-pedagogical skills that allow giving concrete responses to the needs of a globalized society during a human-educational crisis that arises from the COVID-19 pandemic. This study aims at analyzing whether nano-MOOCs improve digital teaching competences in university professors since in the scientific literature, this topic does not show with certainty the effectiveness of these types of courses in teacher training. By conducting a quantitative descriptive-inferential, comparative quasi-experimental research (pre-test and post-test) and with a sample made up of 297 faculty members from Universidad Técnica del Norte (UTN, Ibarra-Ecuador) belonging to the five academic units that compose it, it was identified that the teaching staff has limitations in two of the areas of competence that are articulated by INTEF Common Framework: creation of digital content and security; nevertheless, they did show optimal skills in the areas of information and information literacy, communication and collaboration, and problem solving. The findings also determined that online training based on a nano-MOOC format becomes a successful alternative for university faculty training, 83.84% of the participants under study improved their level of digital competence. These results show that an efficient customizable training can be achieved in less time and adjusted to the needs and characteristics of the professors. The criteria of various authors in this field are ratified with this research, it is, therefore, relevant to evaluate the level of digital competence of teachers and, based on that, be able to plan a personalized training program

Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs

Since their ‘official’ emergence in 2012 (Gardner and Brooks 2018), massive open online courses (MOOCs) have been growing rapidly. They offer low-cost education for both students and content providers; however, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). The most recent literature on MOOCs focuses on identifying factors that contribute to student success, completion level and engagement. One of the MOOC platforms’ ultimate targets is to become self-sustaining, enabling partners to create revenues and offset operating costs. Nevertheless, analysing learners’ purchasing behaviour on MOOCs remains limited. Thus, this study aims to predict students purchasing behaviour and therefore a MOOCs revenue, based on the rich array of activity clickstream and demographic data from learners. Specifically, we compare how several machine learning algorithms, namely RandomForest, GradientBoosting, AdaBoost and XGBoost can predict course purchasability using a large-scale data collection of 23 runs spread over 5 courses delivered by The University of Warwick between 2013 and 2017 via FutureLearn. We further identify the common representative predictive attributes that influence a learner’s certificate purchasing decisions. Our proposed model achieved promising accuracies, between 0.82 and 0.91, using only the time spent on each step. We further reached higher accuracy of 0.83 to 0.95, adding learner demographics (e.g. gender, age group, level of education, and country) which showed a considerable impact on the model’s performance. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenue