iLOG: Embedding and Validating Empirical Usage Intelligence in Learning Objects

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ABSTRACTWith the proliferation of learning objects (LOs) online, both teachers and students are often frustrated in locating those that will meet their specific instructional and learning needs. A key component of this problem is that learning objects that are currently available typically are not based on learning research, do not contain embedded guidance on how they should be used, and do not adhere to common standards. Thus, the long-range goal of the project research team is to augment LOs with empirical usage intelligence how an LO should be used, how it has been used, and how it has impacted instruction and learning that will result in radical improvements in learning and instruction. With this embedded intelligence, learners and teachers will be able to identify the LOs that match their needs, educational and experiential backgrounds, and mode of learning or teaching. Learning management systems will also be able to more effectively sequence learning objects to build courses. To take a significant step toward this long-range goal, this project will be guided by an integrated and multidisciplinary approach in pursuit of the following specific technology and learning goals:Technology Goal 1: Create an Intelligent Learning Object Guide (iLOG) that tracks, diagnoses, and tags the empirical usage intelligence of learning objects.Technology Goal 2: Revise and convert the online course materials for an undergraduate introductory CS course (CS1, already developed at the University of Nebraska-Lincoln [UNL]) into learning objects.Learning Goal 1: Identify the salient learner attributes and content/pedagogical characteristics that can be empirically tracked to impact learning. Learning Goal 2: Measure the impact of active learning and elaborative feedback on student learning with learning objects.In terms of technical innovations, our proposed project will (1) add to the Shareable Content Object Reference Model (SCORM) metadata standard to include empirical usage history and statistics on each learning object, (2) result in a framework and a software system (i.e., iLOG) to empower learning objects with empirical usage intelligence, (3) develop advanced computer-based tracking and analysis tools that provide robust quantitative information on student understanding and learning progress and (4) develop SCORM-compliant learning objects for the CS1 course that are interoperable across a variety of platforms and Learning Management Systems. In terms of research advances, our proposed project will advance our knowledge of (1) student conceptual learning processes, (2) creation of new strategies for using contemporary technology-based instructional approaches, (3) matching of instruction to meet specific needs and preferences of learners, and (4) anomaly diagnosis for intelligent systems such as iLOG interacting with human subjects in uncertain and dynamic learning environments.