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Case 2
Students’ Continuous Assessment through Discussion Threads within Blended Teaching Method (face-to-face and e-learning)

Contributor

Dr. Eleni Mangina

Email

eleni.mangina@ucd.ie

Telephone

+353-1-716-2858

Affiliation

School of Computer Science and Informatics, College of Engineering Mathematical and Physical Sciences, University College Dublin, Belfield, Dublin 4, Ireland.

2.1 Context

This case study describes a 4th year module, Introduction to Artificial Intelligence, that is offered in the BSc in Computer Science, which is a joint degree between the School of Computer Science and Informatics, College of Engineering, Mathematical and Physical Sciences, University College Dublin and the Software School, Fudan University, Shanghai, China.

Following the rapid development of information and communication technology, which has influenced education worldwide, many students have the opportunity to learn any time, anywhere. As the lecturer for the Artificial Intelligence (AI) module for undergraduate students of the Software School at Fudan University, I used a combination of face-to-face and on-line classes due to restrictive timescales for face-to-face lectures and the students’ internship responsibilities in the private sector for three days a week. This blended type of lecturing had to be linked with the assessment criteria by which the students are evaluated in terms of their personal achievement at the conclusion of the module. The assessment of students helps the teacher to evaluate the students’ performance and the effectiveness of the teacher’s effort (Bridges et al.2002).

In contrast to traditional forms of assessment such as, the unseen end of the year examination, the link between the students’ learning activities, the resources and the assessment had to be emphasized clearly (Brown2000). Lectures took place only once a week for three consecutive lecturing hours. The Moodle web-based e-learning environment was used for on-line activities such as, discussion forums, during which I could be in contact with the whole class, despite the fact that there was face-to-face contact only once a week (Rowntree1995). Apart from assignments and a final examination, assessment tasks included contributions to the on-line discussion threads. These mixed modes of assessment reflected the blended approach to teaching (i.e., face-to-face and e-learning).

Each week, the material from the lectures was made available, assessment tasks for their discussion threads were pre-defined and the discussion threads were monitored within the forums assigned by the lecturer (Rossman1999). Group work was also given during the face-to-face classes in order to encourage active learning within the class. The rationale behind this strategy was that the students’ final marks should be representative of their whole effort during the module.

2.2 Learning Outcomes being Assessed

2.2.1 Broad-based learning outcome

At the end of this module the students should be able to demonstrate knowledge of different AI-based software systems and to evaluate them.

2.2.2 Learning objectives

At the end of this module the students should be able to:

2.3 Assessment Procedures/Details

The marking scheme for each module is constructed as follows: 50% exam paper, 20% assignment, and 30% weekly participation and forum questions.

The students were informed, during the first lecture, of the standard rules concerning plagiarism and copyright issues. They had to read the announcements and postings for each module on a weekly basis, attend lectures, study the lecture notes and the recommended chapters from the textbook and answer the initial question in the forum posted from the lecturer by the end of the third day of classes. A maximum of three contributions to each discussion thread was allowed from each student, taking into account that one-liners would not be counted as participation. The discussion threads proved to be knowledge constructive for the students. Especially for the topics related to the software development, the students could share concerns and solve problems through the e-collaborative environment that they created though their postings in the forum. The assessment of the quality of the students’ participation in the online discussions was based on the evaluation of the three most important criteria in discussion threads: ‘Participation in Discussion’ (level of interaction and provision of new information for the discussion thread), ‘Content of Posting’ (level of understanding of the topic and provision of responses based on research) and ‘Critical Thinking’ evidenced by posting (level of critical analysis of a posted idea and justification/explanation of any comments posted). This assessment took place at the end of each week and the e-moderator (lecturer in this case) reviewed the students’ overall participation.

2.4 Strengths and Limitations

2.4.1 Strengths

2.4.2 Limitations

2.5 Contributor’s Reflections on the Assessment

Student feedback on the module was very positive and encouraged me to use the web-based environment in other modules. This led to other colleagues adapting this method for their own use. Reflecting upon this combination of teaching strategy, I found it fruitful, exciting and creative. Apart from the active face-to-face and on-line environment, the progress of the students over the nine weeks could be monitored, and the most proactive and hard-working students could be identified from continuous assessment. The task of e-moderation has been the most stressful one.

Good student performance, measured by student assessment results, depends on effective teaching strategies and module organization and the learning styles of the individual students. Further work might include the clustering of assignments in terms of students’ learning styles, whereby, the students can choose one form of assessment over another based on their own preferences.

2.6 Bibliography

Table 2.1: Module details

Module Title

Introduction to Artificial Intelligence

Degree Programme

BSc in Computer Science (UCD-FUDAN)

Level/Year

4

Teaching/Learning Activities

  • Case studies of AI applications (Knowledge Based Systems, Intelligent Engineering Applications, etc.)
  • Case Based Reasoning
  • Game Playing
  • Genetic Algorithms
  • Machine Learning (Artificial Neural Networks)
  • Problem Solving—Knowledge Based Systems
  • Search algorithms
  • Knowledge Representation
  • Introduction to AI (Roots and Scopes)

Curriculum Outcomes

  • A: 1st Evaluate, compare and design a diagnostic expert system
  • B: 2.1 Use JESS or CLIPS and demonstrate the use of it within a certain intelligent application and test it
  • C: 2.2 Explain and recognise the value of artificial learning methods and classify them based on different applications
  • D: 3rd / Pass Define and describe the basic AI related concepts

Assessment Tasks

  • Assignment, Demo
  • Group Work, Discussion Thread
  • Examination, Assignment, Discussion Thread
  • Examination, Discussion Thread