Students’ perceptions of creativity in learning Information Technology (IT) in project groups

This paper explores students’ perceptions of creativity in learning Information Technology (IT) in project groups and the implications of better educating creative IT students for the future. Theoretically, the extension of social psychology research into creativity lays the basis of bringing creativity, learning and IT education into one framework. Empirically, qualitative interviews were carried out with 48 students from three disciplines, including Computer Science (n = 16), Electronic Systems (n = 15) and Medialogy (n = 17) at Aalborg University (AAU) in Denmark, which has a tradition of using problem-based learning (PBL) in student project groups. According to the findings, the students’ perceptions of creativity reflect their domain-related conceptualization and tacit learning experience, with different levels of confidence of being creative persons. As IT plays multiple roles in developing students’ creativity, it can be regarded as a ‘learning partner’. This implies that in the future creativity should be taught more explicitly, helping students to become creative IT talents as a part of their professional identity. It also requires teaching efforts to build a learning environment that stimulates creativity more effectively through more interactions between learners, learning tasks and learning tools.     

Assessing creative problem-solving with automated text grading

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational–statistical machine learning methods to grade students’ natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students’ constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students’ natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen’s Kappa = .65–.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson’s r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.     

The role of feedback and self-efficacy on web-based learning: The social cognitive perspective

The social cognitive perspective of self-regulated learning suggests that effective learning is determined by the interactions among personal, behavioral, and environmental influences; particularly, high self-regulated learners hold higher motivation (personal), apply better learning strategies (behavioral) and respond to environmental demand more appropriately (environmental). The study thus uses the social cognitive perspective to explore the role of self-efficacy (personal), student feedback behavior, use of learning strategies (behavioral), performance and receiving feedback (environmental) in Web-based learning. There were 76 university students participated in this study. Both quantitative and qualitative methods were applied for data analysis. The results supported that self-efficacy predicted student use of learning strategies and related to elaborated feedback behavior (personal → behavioral). High self-efficacy students applied more high-level learning strategies, such as elaborative strategy and critical thinking. Students who provided elaborated feedback also had higher self-efficacy than those who did not. Moreover, receiving elaborative feedback significantly promoted student self-efficacy (environmental → personal), while receiving knowledge of correct response improved student performance. However, the results indicated that feedback behaviors did not predict academic performance, which may be interfered by modeling effects.     

Short- and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance

This study seeks to promote learning in computer-based learning environments utilizing students’ self-directed metacognitive prompts. Such prompts are based on the idea of instructing students to design their own metacognitive scaffolds and learn with them afterward. In a pre-post experimental design, students in the experimental group (n = 35) were instructed to configure their own metacognitive prompts before learning whereas students in the control group (n = 35) learned without prompts. Log file analysis of navigation behavior indicates that students who learned with their individually designed, self-directed prompts visited relevant webpages significantly more often and spent a longer time on them compared with students in the control group. Moreover, participants in the experimental group attained better transfer performance immediately after learning. The long-term effect in transfer performance was even greater in a follow-up learning session conducted after three weeks without any instructional support in either group. These results are consistent with theories of metacognition and self-regulated learning and indicate that self-directed prompts can lead to sustainable effects.     

Not everybody needs help to seek help: Surprising effects of metacognitive instructions to foster help-seeking in an online-learning environment

Offering help functions is a standard feature of computer-based interactive learning environments (ILE). Nevertheless, a number of recent studies indicate that learners are not using such help facilities effectively. We compared the effects of different metacognitive supports to foster learners’ help-seeking behavior in an ILE for plant identification. Four groups of university students (n = 51), each receiving a different metacognitive instruction, had to determine living plants. They had to think-aloud and were video recorded during the experiment. At the end of the session they completed a knowledge test. The surprising effect was that students in all groups were effective help-seekers. They adapted their help-seeking behavior to the complexity of the plants in an effective way. The results indicate that for students on university level effective help-seeking seems to depend largely on motivational factors.     

Effects of visual feedback on medical students’ procrastination within web-based planning and reflection protocols

Procrastination is a very common problem among students that results from ineffective selfregulation. In two field-experimental studies (N = 18 and N = 49), we investigated whether visual feedback on students’ previous procrastination was effective in provoking a decrease in students’ future procrastination as well as improvements in self-regulated learning. The visual feedback was implemented as a dynamic line chart in a web-based planning and reflection protocol used once a week by medical students to record their class preparation and homework once a week. In the protocols, the students planned and reflected on their personal learning processes and they estimated retrospectively their inclination to procrastinate. The results of both studies consistently showed that presenting students a line chart that adaptively visualizes the course and extent of their self-reported previous procrastination led to a statistically significant and practically relevant decrease in their future procrastination. Furthermore, the visualization had positive effects on other variables central to self-regulated learning. The studies provide converging evidence that the inclination to procrastinate can successfully be counteracted both by a parsimonious and easy-to-implement method. They are suggestive of ways how Internet technology can be used support students’ self-regulated learning.     

Exploring feedback and student characteristics relevant for personalizing feedback strategies

Personalized tutoring feedback is a powerful method that expert human tutors apply when helping students to optimize their learning. Thus, research on tutoring feedback strategies tailoring feedback according to important factors of the learning process has been recognized as a promising issue in the field of computer-based adaptive educational technologies. Our paper seeks to contribute to this area of research by addressing the following aspects: First, to investigate how students' gender, prior knowledge, and motivational characteristics relate to learning outcomes (knowledge gain and changes in motivation). Second, to investigate the impact of these student characteristics on how tutoring feedback strategies varying in content (procedural vs. conceptual) and specificity (concise hints vs. elaborated explanations) of tutoring feedback messages affect students' learning and motivation. Third, to explore the influence of the feedback parameters and student characteristics on students' immediate post-feedback behaviour (skipping vs. trying to accomplish a task, and failing vs. succeeding in providing a correct answer). To address these issues, detailed log-file analyses of an experimental study have been conducted. In this study, 124 sixth and seventh graders have been exposed to various tutoring feedback strategies while working on multi-trial error correction tasks in the domain of fraction arithmetic. The web-based intelligent learning environment ActiveMath was used to present the fraction tasks and trace students' progress and activities. The results reveal that gender is an important factor for feedback efficiency: Male students achieve significantly lower knowledge gains than female students under all tutoring feedback conditions (particularly, under feedback strategies starting with a conceptual hint). Moreover, perceived competence declines from pre- to post-test significantly more for boys than for girls. Yet, the decline in perceived competence is not accompanied by a decline in intrinsic motivation, which, instead, increases significantly from pre- to post-test. With regard to the post-feedback behaviour, the results indicate that students skip further attempts more frequently after conceptual than after procedural feedback messages.     

Credit rating by hybrid machine learning techniques

It is very important for financial institutions to develop credit rating systems to help them to decide whether to grant credit to consumers before issuing loans. In literature, statistical and machine learning techniques for credit rating have been extensively studied. Recent studies focusing on hybrid models by combining different machine learning techniques have shown promising results. However, there are various types of combination methods to develop hybrid models. It is unknown that which hybrid machine learning model can perform the best in credit rating. In this paper, four different types of hybrid models are compared by ‘Classification + Classification’, ‘Classification + Clustering’, ‘Clustering + Classification’, and ‘Clustering + Clustering’ techniques, respectively. A real world dataset from a bank in Taiwan is considered for the experiment. The experimental results show that the ‘Classification + Classification’ hybrid model based on the combination of logistic regression and neural networks can provide the highest prediction accuracy and maximize the profit.     

Learning Java: a programmed instruction approach using Applets

A programmed instruction approach to knowledge acquisition is operationalized by student–teacher interactions that involve monitoring and managing the moment-by-moment progress of a learner throughout the process of achieving a criterion of mastery in a knowledge domain. The teacher is generic and may include another person, a structured text, or a computer. When the steps and increments leading to a task's completion and mastery can be enumerated, the process of interaction-based learning lends itself to implementation with computer-based tutoring systems. The present paper describes a computer-based programmed instruction tutoring system that teaches a learner how to write a simple Java™ computer program. The system is based on a series of Java Applets, which are computer programs that are downloaded from a network server and executed by a client browser such as Netscape®. The use of Java Applets makes the tutoring system available to learners over the World Wide Web. Performance acquisition data are presented to show an individual learner's progression to task completion and mastery using the tutoring system. Survey data are presented to show the subjective responses of a class of students to the use of the tutoring system. The adoption of this computer-based tutoring system is justified as one component within a personalized system of instruction that also includes lectures and collaborative learning experiences.     

Student modeling for a web-based self-assessment system

Detecting weaknesses in students’ knowledge may constitute an objective of testing. Computerized test systems, which can be tailored to students’ knowledge level, are appropriate to realize this objective. These systems are not only used to reveal the students’ knowledge, but also help tutors understand the problems in the educational process. This paper reviews the student modeling problem for computer-based test systems, and also proposes a novel method for the graphical representation of student knowledge. First, we present our test practice system ‘Intelligent’, which is followed by the evaluation of the system in real classroom conditions as well as the effect over students’ knowledge acquisition.     

Student modeling and assessment in intelligent tutoring of software patterns

This paper presents the design, implementation, and evaluation of a student model in DEPTHS (Design Pattern Teaching Help System), an intelligent tutoring system for learning software design patterns. There are many approaches and technologies for student modeling, but choosing the right one depends on intended functionalities of an intelligent system that the student model is going to be used in. Those functionalities often determine the kinds of information that the student model should contain. The student model used in DEPTHS is a result of combining two widely known modeling approaches, namely, stereotype and overlay modeling. The model is domain independent and can be easily applied in other learning domains as well. To keep student model update during the learning process, DEPTHS makes use of a knowledge assessment method based on fuzzy rules (i.e., a combination of production rules and fuzzy logics). The evaluation of DEPTHS performed with the aim of assessing the system’s overall effectiveness and the accuracy of its student model, indicated several advantages of the DEPTHS system over the traditional approach to learning design patterns, and encouraged us to move on further with this research.     

Transforming a teacher education method course through technology: effects on preservice teachers’ technology competency

In this study, an instructional design model was employed for restructuring a teacher education course with technology. The model was applied in a science education method course, which was offered in two different but consecutive semesters with a total enrollment of 111 students in the fall semester and 116 students in the spring semester. Using tools, such as multimedia authoring tools in the fall semester and modeling software in the spring semester, teacher educators designed high quality technology-infused lessons for science and, thereafter, modeled them in classroom for preservice teachers. An assessment instrument was constructed to assess preservice teachers’ technology competency, which was measured in terms of four aspects, namely, (a) selection of appropriate science topics to be taught with technology, (b) use of appropriate technology-supported representations and transformations for science content, (c) use of technology to support teaching strategies, and (d) integration of computer activities with appropriate inquiry-based pedagogy in the science classroom. The results of a MANOVA showed that preservice teachers in the Modeling group outperformed preservice teachers’ overall performance in the Multimedia group, F = 21.534, p = 0.000. More specifically, the Modeling group outperformed the Multimedia group on only two of the four aspects of technology competency, namely, use of technology to support teaching strategies and integration of computer activities with appropriate pedagogy in the classroom, F = 59.893, p = 0.000, and F = 10.943, p = 0.001 respectively. The results indicate that the task of preparing preservice teachers to become technology competent is difficult and requires many efforts for providing them with ample of opportunities during their education to develop the competencies needed to be able to teach with technology.     

Facebook use and academic performance among college students: A mixed-methods study with a multi-ethnic sample

This paper uses a mixed-methods approach to examine the relation between online academic disclosure and academic performance. A multi-ethnic sample of college students (N = 261; male = 66; female = 195; M age ≈ 22 years) responded to open-ended questions about their Facebook use. Thematic analysis revealed that over 14% of the Facebook wall posts/status updates (N = 714) contained academic themes; positive states were more frequent than negative and neutral states and students with lower GPAs expressed negative states more often. A path analysis suggested that academic performance may determine college students’ Facebook use, rather than the reverse. Implications for student support services are discussed.     

A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems

Initializing a student model for individualized tutoring in educational applications is a difficult task, since very little is known about a new student. On the other hand, fast and efficient initialization of the student model is necessary. Otherwise the tutoring system may lose its credibility in the first interactions with the student. In this paper we describe a framework for the initialization of student models in Web-based educational applications. The framework is called ISM. The basic idea of ISM is to set initial values for all aspects of student models using an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm. In particular, a student is first assigned to a stereotype category concerning her/his knowledge level of the domain being taught. Then, the model of the new student is initialized by applying the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. ISM has been applied in a language learning system, which has been used as a test-bed. The quality of the student models created using ISM has been evaluated in an experiment involving classroom students and their teachers. The results from this experiment showed that the initialization of student models was improved using the ISM framework.     

Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment

Learner modeling has been used in computer-based learning environments to model learners’ domain knowledge, cognitive skills, and interests, and customize their experiences in the environment based on this information. In this paper, we develop a learner modeling and adaptive scaffolding framework for Computational Thinking using Simulation and Modeling (CTSiM)—an open ended learning environment that supports synergistic learning of science and Computational Thinking (CT) for middle school students. In CTSiM, students have the freedom to choose and coordinate use of the different tools provided in the environment, as they build and test their models. However, the open-ended nature of the environment makes it hard to interpret the intent of students’ actions, and to provide useful feedback and hints that improves student understanding and helps them achieve their learning goals. To address this challenge, we define an extended learner modeling scheme that uses (1) a hierarchical task model for the CTSiM environment, (2) a set of strategies that support effective learning and model building, and (3) effectiveness and coherence measures that help us evaluate student’s proficiency in the different tasks and strategies. We use this scheme to dynamically scaffold learners when they are deficient in performing their tasks, or they demonstrate suboptimal use of strategies. We demonstrate the effectiveness of our approach in a classroom study where one group of 6th grade students received scaffolding and the other did not. We found that students who received scaffolding built more accurate models, used modeling strategies effectively, adopted more useful modeling behaviors, showed a better understanding of important science and CT concepts, and transferred their modeling skills better to new scenarios.     

Pre-service teachers' experiences of using social software applications for collaborative inquiry

This case study focuses on the use of social software applications for supporting pre-service teachers' collaborative inquiry. Second year pre-service teachers (N = 98) were instructed to carry out an inquiry using a selection of suitable applications to support their work in a forest ecology field course. Although they were not specifically instructed on the use of the software applications, the assumption was that being ‘digital natives’ they would be familiar with them or would readily adopt them. The study reports the students' experiences of the course. Results show variations in their experiences: differences in the way they saw the adequacy of their ICT skills, the suitability of the software, and in their opinions about familiarizing themselves with the software applications as part of the inquiry process. The results also indicate that in future it is important to: (i) concretize and explain the pedagogical aspects of using social software applications as part of the learning process; (ii) highlight the reasons why software applications are used for supporting learning, i.e., the added value; and (iii) encourage students to explore and make use of different software applications for different learning purposes.     

Level of interactivity and executive functions as predictors of learning in computer-based chemistry simulations

High school students’ learning outcomes was examined comparing exploratory vs. worked simulations. The effects of added icons and students’ executive functions were also examined. In Study 1, urban high school students (N = 84) were randomly assigned to one of four versions of a web-based simulation of kinetic molecular theory that varied in instructional format (exploratory vs. worked simulation) and representation (added icons vs. no added icons). Learning was assessed at two levels: comprehension and transfer. For transfer, a main effect was found for instructional format: the exploratory condition yielded greater levels of transfer than the worked simulation. Study 2 used the same conditions and a more complex simulation, the ideal gas law, with a similar sample of students (N = 67). For transfer, an interaction between instructional format and executive functions was found: Whereas students with higher levels of executive functions had better transfer with the exploratory condition, students with lower levels of executive functions had better transfer with the guided simulations. Results are discussed in relation to current theories of instructional design and learning.     

Animated agents in K-12 engineering outreach: Preferred agent characteristics across age levels

Animated agents have been found to positively impact student learning and/or perceptions within computer-based learning environments. However, there is little research on the agent characteristics preferred by K-12 students. The main purpose of this study was to examine student preferences for individual pedagogical agents and their preferences for various agent characteristics. Student preferences for the following agent characteristics were assessed using survey methodology: agent gender, age, realism, clothing, personality, speech pace, and tutoring approach. A total of N = 565 students from elementary through high school watched a computer-based multimedia overview of engineering. Four engineering disciplines were introduced by four animated agents: a young female, young male, old female, and old male agent. Immediately after viewing the computer-based overview, students completed surveys assessing preferences for the four agents and for individual agent characteristics. Results indicated that all students preferred agents and specific external agent characteristics that are close to their own external characteristics and favored internal agent characteristics that they felt would promote understanding of the domain. These findings suggest that animated agents used in computer-based K-12 engineering outreach should be close to the student’s external characteristics.     

Effects of captions and time-compressed video on learner performance and satisfaction

Digital video is becoming increasingly popular in higher education with faculty digitally recording and broadcasting lectures for students to learn-on-demand, such as iTunes University or YouTube. Students have discovered accelerated playback features in popular computer software and use it to reduce the amount of time watching video-enhanced instruction. In the current study, 147 undergraduate students were randomly assigned to one of six video treatments based on a 3 Video Speed (1.0 = Normal vs. 1.25 = Fast vs. 1.50 = Very Fast) × 2 Captions (Captions Present vs. Captions Absent) × 2 Trial (Trial 1 vs. Trial 2) design. Results show no significant difference on learner performance across treatments based on Video Speed. Captions were found to have a significant negative effect on learner performance. A significant difference was found on learner satisfaction in favor of a normal Video Speed. The findings suggest that learners might be able to accelerate Video Speeds up to 1.5 times the normal speed, but are generally less satisfied with the learning experience.