This Self-Learning Module (SLM) is prepared so that you, our dear learners, can continue your studies and learn while at home. Activities, questions, directions, exercises, and discussions are carefully stated for you to understand each lesson.
Each SLM is composed of different parts. Each part shall guide you step-by-step as you discover and understand the lesson prepared for you.
Pre-tests are provided to measure your prior knowledge on lessons in each SLM. This will tell you if you need to proceed on completing this module or if you need to ask your facilitator or your teacher’s assistance for better understanding of the lesson. At the end of each module, you need to answer the post-test to self-check your learning. Answer keys are provided for each activity and test. We trust that you will be honest in using these.
Please use this module with care. Do not put unnecessary marks on any part of this SLM. Use a separate sheet of paper in answering the exercises and tests. And read the instructions carefully before performing each task.
If you have any questions in using this SLM or any difficulty in answering the tasks in this module, do not hesitate to consult your teacher or facilitator.
This module was designed and written with you in mind. It is here to help you master Linear and Non-Linear Texts. The scope of this module permits it to be used in many different learning situations. The language used recognizes the diverse vocabulary level of students. The lessons are arranged to follow the standard sequence of the course. But the order in which you read them can be changed to correspond with the textbook you are now using.
The module is divided into two lessons, namely:
- Lesson 1 – Features of Linear & Non-Linear Texts
- Lesson 2 – Trans coding Information from Linear to Non-Linear Texts and Vice-Versa
Most Essential Learning Competency
- Trans code information from linear to non-linear texts and vice-versa.
After going through this module, you are expected to:
1. identify linear and non-linear texts;
2. differentiate linear text from non-linear text;
3. interpret data in non-linear texts;
4. draw data from linear and non-linear texts; and
5. transcode information from linear to non-linear texts and vice-versa.