New technologies

There are many new technologies to be developed in this project.

Classroom 2.0

So what does a “Smart Classroom” should really constitute? The answer should be what the course instructor and students need to order to improve the in class teaching and learning experience. In our view there are four integral parts of a truly smart classroom. The technologies in the classroom should be intelligent, interactive, individualize and integrated as defined in the Lead Project. Below we give an overview of our proposed research that together will define the i4-future classrooms. We call those classrooms as Classroom 2.0.

1. Intelligent: the classroom technology should automatically conduct tasks that don’t need human interventions.

(1) The intelligent Roll-Call (iRollCall)) system will involve research in face recognition and speaker identification to automatically recognize students in the classroom.
(2) The intelligent Teaching Response (iTRes) system will involve research in body/hand gestures recognition and speech recognition to automatically control the technologies used in the classroom, including the control of the software systems (e.g. PPT).
(3) The intelligent Classroom Exception Recognition (iCERec) system will require research in multi-camera multi-gesture recognition to automatically indentify and track human behaviors in the classroom. This system is intended to automatically track classroom student behaviors and is a revolutionary way to collect data for educational research.
(4) The intelligent Content Retrieval (iCORE) system will require joint research effort in image annotation, speech transcription, text understanding and data mining to automatically give a feedback on the goodness of an answer to a given question.

2. Interactive: the classroom technology should facilitate interactions between classroom instructor and the students.

(1) The iRollCall system allows the classroom instructor to call on students on the first name basis, which was shown to be a key to improving student-teacher interactivities in past researches.
(2) The iTRes system does not allow for student-teacher interaction. However, it does enable better human-computer interaction, which would enhance the flow of the in-class learning activities.

3. Individualize: the classroom technology should react differently in accordance to individual user.

(1) The iCORE system will give distinct feedback to Q/A posed by different student.
(2) The iCERec system in the long term can learn each student’s unique body gesture and thus enable different classroom tracking mechanism.

4. Integrated: the classroom technologies should be integrated as one i4 system instead of many separate systems.

(1) All the systems are software based; all will be integrated into the i4 future classroom environment that we call Classroom 2.0.
(2) The research results from the Mobile 2.0 project will be integrated into the Classroom 2.0 environment to allow for more interactivities in the classroom.

We propose the use of emerging and prevalent handheld devices to further extend the modern classroom to a truly enjoyable learning environment, which is intelligent, interactive, individualize, and integrated (i4future). Below we give an overview of our proposed research that together will define the i4-future learning environments. We call those classrooms as Mobile 2.0.

1. Intelligent: the Mobile 2.0 technology is able to adapt itself to facilitate teaching and learning activities in the i4-future learning environments.

(1) The Intelligent Auto-Configured Environment (iACE) will involve research in autonomic computing of handheld devices, and the resulting lecturing environment is self-configurable, self-healing, self-optimized, and self-protected.
(2) The Intelligent Quality Assured Mobile System (iQAMS) will involve research in adaptive rate control techniques for data transmission in the i4-future learning environments, and agile transcoding techniques for audio, video, and documents.
(3) The Intelligent Reliable and Secure Mobile Platform (iRSMP) will involve research in network security techniques that can automatically detect malicious attacks and adapt itself to secure data transmission in the i4-future learning environments.

2. Interactive: the Mobile 2.0 technology should facilitate the instructor-students and students-students interactions.

(1) The Intelligent Auto-Configured Environment (iACE) will provide an efficient inter-networking environment that allows interactions taking place between the instructor and the students, and/or between students and students.
(2) The Intelligent Quality Assured Mobile System (iQAMS) will facilitate interactions taking place in the class by assuring the quality of data transmission in the lecturing environments.
(3) The Intelligent Reliable and Secure Mobile Platform (iRSMP) will facilitate interactions taking place in the class by ensuring the privacy and security of data transmission in the lecturing environments.

3. Individualize: the Mobile 2.0 technology should react differently in accordance to individual user.

(1) The Intelligent Auto-Configured Environment (iACE) will provide rich network connectivity such that students can do individualized learning by searching relevant class materials from the Internet, archiving\ ongoing class materials, looking up previously stored class materials, and asking the instructors/classmates questions.
(2) The Intelligent Quality Assured Mobile System (iQAMS) will provide individualized data transmission service (e.g., transcoding class materials in accordance with the screen resolution and supported number of colors of the used handheld devices) in the learning environments.
(3) The Intelligent Reliable and Secure Mobile Platform (iRSMP) will provide secure and anonymous data transmission for individuals in the network to interact with the others.

4. Integrated: the Mobile 2.0 technologies should be integrated as one i4 system instead of many separate systems.

(1) All the systems are software based; all will be integrated into the i4 future lecturing environment that we call Mobile 2.0.
(2) The Mobile 2.0 project will work closely with the Classroom 2.0 project. Some of the results from the Mobile 2.0 project will be integrated into the Classroom 2.0 environment to allow further interactivities in the classroom.
(3) The Mobile 2.0 project will work closely with the Testing 2.0 project. Some of the results from the Testing 2.0 project will be carried out and evaluated on the Mobile 2.0 environment

Testing 2.0 will be pioneering new technologies such as machine learning and translation and natural language processing to improve assessment tools in terms of content, formats, scoring and analysis methods. In addition, this particular project, including two sub-projects, also tries to address related issues such as the use of machine learning, text processing and user modeling technologies to develop interactive and intelligent tools for supporting tasks of item authoring, response grading, grade reporting, and item banking in educational testing. Thorough behavioral and educational task analysis will also be conducted as the foundation for technological innovation. The technologies that will be studied in this project also include machine translation for converting international tests into traditional Chinese items, text mining and retrieval for intelligent item access, duplication detection, item categorization, and item augmentation and expansion. Together with these underlying technologies and current Web technologies such as Web 2.0, XML, and AJAX, Testing 2.0 shall develop a state-of-the-art item banking system to achieve the research objectives.

An experimental design involving three groups will possibly be employed in the main project. The design is illustrated inillustrated in Figure 3.

Figure 3. The experimental design employed by the main project

A number of variables, such as the number of university and senior high school students, levels of science background, and the equivalent instructional content and duration, will be held constant. The major independent variable will be the format of learning environment modes and the dependent variables would be TTA, SLS, STI, and SLO. A multivariate analysis of covariance (MANCOVA) will be performed on the dependent variables with pre-treatment measures as the covariates to find any significant differences between the groups. The Wilks’s lambda (λ) test will be employed to test the difference between the groups on the set of learning outcomes post-test means. The level of confidence will be set at a 0.05 level of significance.

To further understand how the groups would be affected by the interventions, we will carry out univariate analysis of covariance (ANCOVA) on posttest scores (with the pretest as the covariate) on the individual measures respectively to determine any significance between the groups. To additionally determine the effects of student preferred-actual learning environment spaces (PAS) on their SLS, STI, and SLO in the actual classroom, the students participating in this study will also be categorized into either the least congruent group (LC) or the most congruent group (MC) based on their average PAS
scores, respectively. A multivariate analysis of covariance (MANCOVA) will be again performed on the dependent variables with pre-treatment measures as the covariates to find any significant differences between the LC and MC groups. Several assumptions of the MANCOVA and ANCOVA will be first tested to ensure that they will meet in the analysis of covariance for the present study.

To understand how the match between preferred and actual learning environment accounts for outcome variance beyond that attributable to the actual learning environment, the main project will possibly use the following variables as predictors in stepwise multiple regression models to predict SLS, STI, and SLO:

(1) Pretest scores
(2) PASpre
(3) PASpost
(4) PASmatch
(5) Actual learning environment

Consequently, several regression models using the variables above will be constructed to explain the SLS, STI, and SLO separately. For the regression model, the pretest score will also be included for the regression analysis. Assumptions for the linear regression model will be checked to ensure that they will be met in the analysis of regression model for the main project. The measures of each dependent variable will be needed to be independent. The histogram of the standardized plot implied no violation of the normality assumption and the standardized residual plots will also be used to indicate that assumptions of the linear regression model are tenable.

To meet contemporary calls for improvement in the interpretation and reporting of quantitative research in education (Rennie, 1998; Thompson, 1996), this study will report practical significance (effect magnitudes) along with statistical significance test. The effect size index eta squared (η2) or f will be used since it is more appropriate for the analysis of variance or covariance (Cohen, 1988). According to Cohen’s rough characterization (1988, pp. 284-288), f = 0.1 is deemed as a small effect size, f = 0.25 a medium effect size, and f = 0.4 as the large effect size (for interpreting η2, 0.01 = ‘small,’ 0.059 = ‘medium,’ and 0.138 = ‘large’). This kind of data presentation method is quite important in terms of interpreting research results. Researchers have cautioned the insufficiency of using only the result of statistical significance testing in statistical inference (Cohen, 1988; Daniel, 1998; McLean & Ernest, 1998). This is mainly because the computation of statistical significance is related to the sample size involved in the analysis. Moreover, it is quite common to observe a statistical significance with a large sample size, even if there was little practical effect actually. Therefore, to protect against statistical significant finding in terms of large sample size for the current study, effect magnitudes will also be reported along with statistical significance test. Tests of the assumptions for the ANCOVA will be undertaken using SPSS 13.0 (Statistical Package for Social Sciences version 13.0).