This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text‐based self‐evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information from student self‐evaluations yields a significant improvement in early‐stage prediction quality. The results also indicate the limited early‐stage predictive value of structured data, such as homework completion, attendance, and exam grades, due to data sparseness at the beginning of the course. Thus, applying sentiment analysis to unstructured data (e.g., self‐evaluation comments) can play an important role in improving the accuracy of early‐stage predictions. The findings present educators with an opportunity to provide students with real‐time feedback and support to help students become self‐regulated learners. Using the exploring results for improvement in teaching and learning initiatives is important to maintain students' performances and the effectiveness of the learning process.
Researchers have indicated that the collaborative problem-solving space afforded by the collaborative systems significantly impact the problem-solving process. However, recent investigations into collaborative simulations, which allow a group of students to jointly manipulate a problem in a shared problem space, have yielded divergent results regarding their effects on collaborative learning. Hence, this study analysed how students solved a physics problem using individual-based and collaborative simulations to understand their effects on science learning. Multiple data sources including group discourse, problem-solving activities, learning test scores, and questionnaire feedback were analysed. Lag sequential analysis on the data found that students using the two simulations collaborated with peers to solve the problem in significantly different patterns. The students using the collaborative simulations demonstrated active engagement in the collaborative activity; however, they did not transform discussions into workable problem-solving activities. The students using the individual-based simulation showed a lower level of collaboration engagement, starting with individual exploration of the problem with the simulation, followed by group reflection. The two groups also showed significant differences in their learning test scores. The findings and pedagogical suggestions are discussed in the hope of addressing critical activity design issues in using computer simulations for facilitating collaborative learning.
Industrial Design is concerned with the design of intelligent products and services. When designing these products and services, emotional mediation could be a key aspect in intelligent behavior [2]. In the field of industrial design not much attention has been paid to expressing emotions through movement. Because of this the potency of movement as message carrier and the language of movement were the topic of research. This evoked two questions: Does the addition of movement to objects enrich/enforce the emotional message of the objects? How does a context influence the emotional message of an object? In order to find the answer to these two questions, five vending machines were built. These machines were expressive in their movement and were meant for a specific context. The objects were created using the following idea generation techniques: collages, acting out, tinkering and 4D Sketching [1]. Finally a user test was committed to measure the strength of the message with and without movement. The difference between the strength of the message when the object is in its context and when it is not, was also tested.The result of this test shows an apparent relation between the addition of movement to a static object and an increase of the strenght of its emotional message. The presence of the moving object in its accompanying context does not have a relation, which is as apparent as the addition of movement. One must say though that the addition of a context and movement to an object still has a significant effect, showing that the presence of a context certainly hasn't got negative effects. With the results of this research, hopefully attention will be drawn to the importance of movement in objects that mediate a certain message.
A competency‐based curriculum involves an outcome‐based approach for cultivating graduates’ core competencies required for specific professions. A curriculum committee defines graduates’ outcomes of core competencies, designs a curriculum to cultivate core competencies, evaluates graduates’ outcomes, and reflects on and regulates the curriculum. However, curriculum committees lack systematic evaluative information for reflection. Learning analytics, an emerging data‐driven analytics of educational data, could be applied to assist curriculum committees in reflection. This study proposes competency‐based learning analytics, including seven analytic tools, to analyze curricula and graduates’ academic records to assist curriculum committees in reflecting (1) objectives, competencies, and curriculum design of competency‐based curricula and (2) faculty teaching and student learning. The proposed learning analytics were conducted on 14 departments of a university. This study reports the curriculum committees’ eight practical reflections of curricula, faculty teaching, and student learning. This study illustrates potential applications and impact of competency‐based learning analytics on competency‐based curricula.
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