Аналіз відмінностей тем, якості письма та стилістичного контексту в есеях студентів коледжу на основі комп’ютерної програми Linguistic Inquiry and Word Count (LIWC).

Machine methods for automatically analyzing text have been investigated for decades. Yet the availability and usability of these methods for classifying and scoring specialized essays in small samples–as is typical for ordinary coursework–remains unclear. In this paper we analyzed 156 essays submitted by students in a first-year college rhetoric course. Using cognitive and affective measures within Linguistic Inquiry and Word Count (LIWC), we tested whether machine analyses could i) distinguish among essay topics, ii) distinguish between high and low writing quality, and iii) identify differences due to changes in rhetorical context across writing assignments. The results showed positive results for all three tests. We consider ways that LIWC may benefit college instructors in assessing student compositions and in monitoring the effectiveness of the course curriculum. We also consider extensions of machine assessments for instructional applications.Машинні методи автоматичного аналізу тексту та їхні можливості вивчалися впродовж десятиліть. Однак питання доступності та зручності використання цих методів для класифікації та оцінки спеціалізованих есеїв у невеликих зразках, як, наприклад, курсових роботах, залишається досі малодослідженим питанням. У статті проаналізовано 139 есеїв із курсу стилістики, написаних студентами першого курсу. На основі використання когнітивних та афективних категорій програми Linguistic Inquiry and Word Count (LIWC) було перевірено здатність машинного аналізу: а) розмежовувати теми есеїв, б) розрізняти високу та низьку якість письма та в) виявляти відмінності через зміни стилістичного контексту написаних завдань. Дослідження засвідчило позитивні результати для всіх трьох тестових перевірок. Увагу авторів зосереджено на тому, як LIWC може полегшити роботу університетських викладачів під час оцінки ними студентських творів та моніторингу ефективності навчальної програми курсу. Крім того, у статті розглянуто питання перспектив машинного оцінювання викладацьких застосунків

Discovering Organizational Correlations from Twitter

Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because important information is usually not publicly available (e.g., the correlations of terrorist organizations). Nowadays, an increasing amount of organizational information can be posted online by individuals and spread instantly through Twitter. Such information can be crucial for detecting organizational correlations. In this paper, we study the problem of discovering correlations among organizations from Twitter. Mining organizational correlations is a very challenging task due to the following reasons: a) Data in Twitter occurs as large volumes of mixed information. The most relevant information about organizations is often buried. Thus, the organizational correlations can be scattered in multiple places, represented by different forms; b) Making use of information from Twitter collectively and judiciously is difficult because of the multiple representations of organizational correlations that are extracted. In order to address these issues, we propose multi-CG (multiple Correlation Graphs based model), an unsupervised framework that can learn a consensus of correlations among organizations based on multiple representations extracted from Twitter, which is more accurate and robust than correlations based on a single representation. Empirical study shows that the consensus graph extracted from Twitter can capture the organizational correlations effectively.Comment: 11 pages, 4 figure

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin

The measurement of enhancement in mathematical abilities as a result of joint cognitive trainings in numerical and visual-spatial skills: A preliminary study

A body of literature shows the significant role of visual-spatial skills played in the improvement of mathematical skills in the primary school. The main goal of the current study was to investigate the impact of a combined visuo-spatial and mathematical training on the improvement of mathematical skills in 146 second graders of several schools located in Italy. Participants were presented single pencil-and-paper visuo-spatial or mathematical trainings, computerised version of the above mentioned treatments, as well as a combined version of computer-assisted and pencil-and-paper visuo-spatial and mathematical trainings, respectively. Experimental groups were presented with training for 3 months, once a week. All children were treated collectively both in computer-assisted or pencil-and-paper modalities. At pre and post-test all our participants were presented with a battery of objective tests assessing numerical and visuo-spatial abilities. Our results suggest the positive effect of different types of training for the empowerment of visuo-spatial and numerical abilities. Specifically, the combination of computerised and pencil-and-paper versions of visuo-spatial and mathematical trainings are more effective than the single execution of the software or of the pencil-and-paper treatment

A collaborative and experiential learning model powered by real-world projects

Information Technology (IT) curricula\u27s strong application component and its focus on user centeredness and team work require that students experience directly real-world projects for real users of IT solutions. Although the merit of this IT educational tenet is universally recognized, delivering collaborative and experiential learning has its challenges. Reaching out to identify projects formulated by actual organizations adds significantly to course preparation. There is a certain level of risk involved with delivering a useful solution while, at the same time, enough room should be allowed for students to experiment with, be wrong about, review, and learn. Challenges pertaining to the real-world aspect of problem-based learning are compounded by managing student teams and assessing their work such that both individual and collective contributions are taken into account. Finally, the quality of the project releases is not the only measure of student learning. Students should be given meaningful opportunities to practice, improve, and demonstrate their communication and interpersonal skills. In this paper we present our experience with two courses in which teams of students worked on real-world projects involving three external partners. We describe how each of the challenges listed above has impacted the course requirements, class instruction, team dynamics, assessment, and learning in these courses. Course assessment and survey data from students are linked to learning outcomes and point to areas where the collaborative and experiential learning model needs improvement