Use of a smartphone-based Student Response System in large active-learning Chemical Engineering Thermodynamics classrooms

Smartphones are a promising tool as student response systems (SRS) for interactive teaching due to their widespread diffusion. Here, the main purpose is to assess the efficacy of smartphone-based SRS in large classroom settings of undergraduate Thermodynamics, as representative of engineering courses requiring high-level cognitive skills for problem solving. Four sets of multiple-choice questions were presented during the course. Overall, the results refer to 1055 students between control and SRS classes, each corresponding to a3 years period.One of the main results of this work is the strong linear correlation between the average questionnaire score and the final exam grade (R² = 0.91). A similar correlation, although with a lower value of R², is already found in the first questionnaire, thus showing the SRS high predictive power of class performance. The results of this study provide guidance for a quantitative use of smartphone-based SRS in teaching basic disciplines. The SRS monitoring capability allows early detection of struggling students, thus paving the way to personalized tutoring and improved student engagement in active learning practices. This approach is especially important in emergency situations, such as the SARS-Cov-2 pandemic, when distance learning is widely adopted, and remote interactive tools are highly needed.     

Utilizing sensor data to model students’ creativity in a digital environment

While creativity is essential for developing students’ broad expertise in Science, Technology, Engineering, and Math (STEM) fields, many students struggle with various aspects of being creative. Digital technologies have the unique opportunity to support the creative process by (1) recognizing elements of students’ creativity, such as when creativity is lacking (modeling step), and (2) providing tailored scaffolding based on that information (intervention step). However, to date little work exists on either of these aspects. Here, we focus on the modeling step. Specifically, we explore the utility of various sensing devices, including an eye tracker, a skin conductance bracelet, and an EEG sensor, for modeling creativity during an educational activity, namely geometry proof generation. We found reliable differences in sensor features characterizing low vs. high creativity students. We then applied machine learning to build classifiers that achieved good accuracy in distinguishing these two student groups, providing evidence that sensor features are valuable for modeling creativity.     

Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction

We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the student’s behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the student’s knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.     

项目式教学在职业教育中对学生承诺的考核方法的研究

在中职、高职类院校,项目式教学正在被推广,在项目式教学模式下,我们把实际的企业项目带到课堂,同时,我们也希望借鉴有益的企业对员工的评价方法。基于此,提出在原有的项目式评价学生体系下,增加对学生承诺的考核方法,并给出切实可操作的研究方案。增加对学生承诺的考核旨在充分发掘学生的潜能,对超出学生承诺的专业能力的成绩给予鼓励。     

宁夏司法警官职业学院学生信息管理系统研究

长期以来,宁夏司法警官职业学院各种信息通过Office软件来存储,处理,在信息的更新,共享,查询,版本控制上等方面存在很多问题,管理这些信息比较繁琐,并且信息很难共享,因此学院急需一套信息管理系统来管理这些信息。该文对宁夏司法警官职业学院学生信息管理系统的项目立项、用户需求征询、设计开发、测试实施做了一个比较全面的概述。     

Not All Sources Are Created Equal: Student Research,Source Equivalence

ABSTRACT

With the emergence of the browser searchable Internet in the early 1990s, accessing the World Wide Web has become commonplace. In regards to its impact on student research, current undergraduates have become savvy searchers when it comes to accessing and integrating Internet documents into their research papers. However, this ease of use has come at a price. The overall quality of research being submitted by our students has suffered. Many incoming undergraduates don't make the distinction between documents located via a typical Internet search engine and full-text information generated from academic proprietary databases housed on the library's home page.     

基于工作流技术的学生评语生成系统的设计与实现

工作流就是业务过程的部分或整体在计算机应用环境下的自动化,它主要解决的是使在多个参与者之间按照某种预定义的规则传递文档、信息或任务的过程自动进行,从而实现某个预期的业务目标。对工作流技术的定义、研究现状做分析,对系统主要功能模块的设计进行详细阐述。     

高职计算机专业学生评价体系的探索

在德国职业教育的影响下,高职计算机专业教育开始推行任务驱动,工作过程导向的教育模式,但是其教育评价体系还没有与教学模式相统一,实现与对应就业岗位需求的完美结合。高职计算机专业教育评价体系包含了很多方面,本文主要从学生学习效果的评价体系进行探索。     

基于VB的学生成绩管理系统设计

开发学生成绩管理系统,实现学生成绩的系统化、规范化和自动化,主要功能用计算机对学生成绩管理.     

The relationship between learning styles and cognitive traits – Getting additional information for improving student modelling

Student modelling is an important process for adaptive virtual learning environments. Student models include a range of information about the learners such as their domain competence, learning style or cognitive traits. To be able to adapt to the learners’ needs in an appropriate way, a reliable student model is necessary, but getting enough information about a learner is quite challenging. Therefore, mechanisms are needed to support the detection process of the required information. In this paper, we investigate the relationship between learning styles, in particular, those pertaining to the Felder–Silverman learning style model and working memory capacity, one of the cognitive traits included in the cognitive trait model. The identified relationship is derived from links between learning styles, cognitive styles, and working memory capacity which are based on studies from the literature. As a result, we demonstrate that learners with high working memory capacity tend to prefer a reflective, intuitive, and sequential learning style whereas learners with low working memory capacity tend to prefer an active, sensing, visual, and global learning style. This interaction can be used to improve the student model. Systems which are able to detect either only cognitive traits or only learning styles retrieve additional information through the identified relationship. Otherwise, for systems that already incorporate learning styles and cognitive traits, the interaction can be used to improve the detection process of both by including the additional information of a learning style into the detection process of cognitive traits and vice versa. This leads to a more reliable student model.