Category: Teaching

How to use Google Site to create interactive notes?

Do you want to create interactive notes like SCORM package but using Google Site as the platform? You do not need to use LLMs or Elearning unlike SCORM package.

These are the steps that I used:

  1. Generate HTML code. You can use Claude, ChatGPT or Gemini AI to generate the HTML code.
  2. Copy the HTML code and paste it in Google Site. <Embed>
  3. To share the interactive site with your students, make sure you use the appropriate setting [Published site -> Public]

Here is an editable prompt to generate HTML code using ROTAFA format that you can copy-and-paste to Claude or ChatGPT or Gemini AI (better use Gemini Pro – UTM account).

R — Role

Act as an experienced instructional designer, adult educator, educational content creator and front-end developer. Apply adult-learning principles, inclusive pedagogy, cognitive scaffolding and accessible web-design practices.

O — Objective

Create an engaging, self-paced instructional webpage that helps learners understand [Add course or module topic] and achieve these learning outcomes:

  1. [Learning outcome 1]
  2. [Learning outcome 2]
  3. [Learning outcome 3]

The learning experience must remain understandable to beginners while providing sufficient intellectual depth for learners with an educational or professional background.

T — Task

Develop the complete instructional content and functional webpage. Include:

  • A concise introduction explaining the topic’s relevance.
  • Clearly stated learning outcomes.
  • Well-organised notes divided into short, manageable sections.
  • Plain-language explanations of technical terms.
  • Relevant examples, analogies or brief scenarios.
  • “Key point” boxes highlighting essential ideas.
  • A short summary at the end of each major section.
  • [Add number] multiple-choice questions, each with four plausible options and only one correct answer.
  • Immediate feedback explaining why the selected answer is correct or incorrect.
  • One or two [You can change the number] open-ended reflective questions that connect the topic with learners’ experiences or practice.
  • A final section containing suggested answers or guidance for self-review.
  • References for factual or theoretical content, using [APA 7th/other] style.

Ensure that the questions assess understanding and application, rather than simple recall alone.

If the course topic, learning outcomes, duration or assessment level has not been provided, ask me concise clarification questions before generating the webpage.

A — Audience

The learners range from their 20s to their 60s. Some have formal educational or professional backgrounds, while others have little or no background in education. [You can change the audience to suit with the course that you are teaching]

Use respectful, inclusive and non-patronising language. Do not assume prior knowledge. Introduce concepts progressively, moving from foundational explanations to practical application. Avoid unnecessary jargon and explain unavoidable terminology when it first appears.

F — Format

Produce one complete, self-contained HTML document suitable for embedding in Google Sites through Insert → Embed → Embed code.

Technical requirements:

  • Place all HTML, CSS and JavaScript in one code block.
  • Use semantic HTML with clear heading levels.
  • Make the page responsive for desktop, tablet and mobile screens.
  • Use accessible labels, keyboard-friendly controls and visible focus indicators.
  • Maintain sufficient colour contrast and readable font sizes.
  • Use lightweight vanilla JavaScript only.
  • Do not use external libraries, frameworks or assets.
  • Make the multiple-choice questions interactive and display feedback without reloading the page.
  • Include a “Reset answers” button.
  • Return the finished HTML code only, without explanatory text outside the code block.

A — Additional Requirements

Use a minimalist educational design with:

  • Soft, calming colours such as muted blue, sage green, warm cream and pale lavender.
  • Generous white space and restrained visual decoration.
  • A clean sans-serif font stack.
  • Consistent cards, buttons and section spacing.
  • A maximum content width that supports comfortable reading.
  • Subtle transitions that do not distract learners.
  • No flashing elements, autoplay media or excessive animation.

Course details:

  • Course title: [Insert title]
  • Topic or module: [Insert topic]
  • Learning duration: [Insert estimated duration]
  • Language: [Insert language]
  • Academic level: [Insert level or “mixed”]
  • Number of MCQs: [Insert number]
  • Relevant context or discipline: [Insert context]
  • Content or references to use: [Paste source material, if applicable]

Do not fabricate references, statistics or theories. If reliable source material is required but unavailable, mark the affected section clearly for instructor verification.

You want to see an example?

This is an example of Google Site for a short course that I have to conduct with outsiders (non UTM students or staff) who cannot get accessed to our eLearning.

Additional tips:

  1. If you use Claude, to ensure the information of your interactive note is based on facts or have references, upload your class notes, ebook or relevant materials in Project (Folder). You can also ask Claude to add or use information based on valid references such as journals etc.
  2. If you use Gemini AI or ChatGPT, to ensure the information of your interactive note is based on facts or have references, upload your class notes, ebook or relevant materials (click + button) and write the prompt in the chatbox. Double check that you have uploaded the materials and add another statement in your prompt “Only use information from the provided/uploaded materials ONLY”. This is to reduce “hallucination”.

ROTAFA: Prompt engineering (What’s that?)

I had another session with Prof Karim yesterday night (7 August 2026, Friday) from 825ish pm until 11pm. Yes. It is PM. Night.

Some of us wondered how he had the energy to stay awake and alert past 10pm. I have to say, at my age, it’s not easy to stay up so late and I wasn’t expecting to sleep in a little bit either. Alhamdulillah I woke up just before the subuh azan.

Anyway, one of the things he insists on emphasizing is employing ROTAFA when we want to improve the output from using AI.

This is an example of the prompt that I use to create another video.

[Role]: Act as a veteran Educational Psychology Professor [change this role to yours] who doubles as a popular YouTube/educational content creator (think CrashCourse style).

[Objective]: Teach undergraduate students the core concepts of Interest Theory (specifically Hidi & Renninger’s Four-Phase Model of Interest Development) [change this objective to yours] so they can apply it to classroom teaching and pass their exams.

[Task]: Draft a complete, minute-by-minute instructional video script explaining Interest Theory [change this task to yours] . Break down the distinction between [change this accordingly] Situational Interest and Individual Interest , and show how one transitions into the other.

[Audience]: Undergraduate education and psychology majors [change this accordingly] who prefer engaging, relatable real-world student examples over dense academic jargon.

[Format]: Present the script in a 3-column table:

  1. Timestamp & Section (e.g., 0:00 – 0:45 | Hook)
  2. Visual Cues & Graphics (B-roll, on-screen text, slide diagrams)
  3. Spoken Dialogue / Voiceover (Exact script to read)

[Additional Constraints]:

  • Length: Cap total spoken dialogue at 600–700 words to ensure it comfortably stays under the 5-minute mark at a natural speaking pace (~130–140 wpm).
  • Structure: Include an engaging 30-second hook, 2 concrete real-world student scenarios, a quick recap table, and 1 reflection question at the end.
  • Tone: Energetic, clear, academically grounded, and conversational. Avoid dry textbook fluff.

31 Students, 21 Empty Hands: A Wake-Up Call on Student Reading Habits

A series of quick polls during recent class sessions revealed a jarring reality regarding student engagement with the library. Across more than 60 undergraduate and postgraduate students polled that I teach this semester (Semester 2, Session 2025/2026), over half confessed they had never borrowed a book. This lack of engagement was further highlighted in a class of 31 undergraduates (you know who you are!), where more than half admitted they had not yet borrowed a single textbook this semester when I did a quick survey in the middle of semester.  This immediately raises the question of how many books they actually read each semester. While younger generations may prefer e-books over the physical copies favoured by Gen X like me, the lack of engagement with library resources is striking. When asked if they rely primarily on class notes to understand core concepts, the majority answered yes. This trend is alarming.

Reading books does not make one a “nerd.” Yet, students are becoming overly reliant on lecture notes, which are merely summarized shortcuts of a lecturer’s own reading. Lecture slides are designed to guide, not to replace actual reading. True intellectual growth and critical thinking are unlocked only through active, independent reading. While many students believe notes are sufficient for good grades, true learning requires active cognitive engagement. Relying solely on summaries leads directly to “The Spoon-Feeding” Trap, where students rely on someone else’s digested knowledge rather than processing information themselves.

When I was an undergraduate, I had lecturers who never wrote a single word on the whiteboard nor provided lecture notes. This approach forced me to read textbooks before class, preparing notes and questions that I would ask during class. Without that preparation, passing those courses would have been almost impossible. In courses with multiple references, such as history or philosophy, I was pushed to read extensively, even when the material was difficult to grasp. Coming to class prepared significantly enhanced my understanding. 

For example, when first introduced to operant conditioning, I wondered how it applies in real life. This intellectual curiosity prompted me to seek out books by B.F. Skinner. I discovered his 1948 utopian novel, Walden Two, which depicts a community structured entirely around positive reinforcement. The book inspired the founding of the Twin Oaks Community in Virginia in 1967 (which still exists today). This community designed a society that avoids punishment, instead using a “labour credit” system (a form of token economy) where less desirable jobs are reinforced with higher credits. Reading Walden Two alongside his non-fiction works, Beyond Freedom and Dignity (1971) and About Behaviourism (1974), helped me understand these principles, but also to recognize behaviourism’s limitations. After grappling with the Western perspectives on reward and punishment, I realized I needed a philosophical counterweight. This intellectual curiosity led me to seek out and read Malik Badri’s The Dilemma of Muslim Psychologists (1979), a pursuit encouraged by some of my own lecturers, the very same ones who never wrote anything on the whiteboard nor used any lecture slaid.

The process of reading both Western and Muslim thinkers proved invaluable, opening my mind to truly multi-dimensional understandings of psychological concepts. The deepest insight came from realizing the stark contrast with strict behaviourism: from an Islamic perspective, rewards and punishments are fundamentally rooted in Allah’s Mercy. It is important to note that continuous worldly success can sometimes be a test (istidraj) rather than a straightforward reward. This concept immediately introduced a profound ethical and spiritual nuance, challenging the behaviourist premise that outcomes are simply tied to behavioural reinforcement, irrespective of any higher moral or spiritual value.

Personally, I believe reading provides the crucial context, history, and nuance that bullet points on a PowerPoint slide always leave out. I view reading not as a chore for exams, but as a path to personal growth. I am a strong believer that reading pushes students from surface-level learning toward meaningful learning, transforming them from passive consumers into active learners. Even in our fast-paced AI era, where reading a book from cover-to-cover might feel like a lost art, there is just something magical about getting lost in a good book. That feeling of being transported to another world is something a quick summary video simply cannot match!

It was great to see the quick poll (and maybe a little “friendly” nudge!) got some students thinking. A few even came to me eager to know how to boost their reading habits. Remember, just like building muscle at the gym, improving any skill requires consistent action, not just a burst of motivation. While reading does require a little “cognitive stamina” (think of it as a mental muscle!) it is absolutely something we can build. The key is turning that initial excitement into an easy, stress-free, and sustainable habit.

Here are a few starting actions:

●          Start small: Read just 5 to 10 pages a day of a textbook or academic chapter instead of attempting to finish a whole book in one sitting. If thick textbooks or journal articles are intimidating, start with non-fiction books related to the courses that you are taking.

●          Create a friction-free environment: With the distraction of smartphones and short videos from TikTok and Instagram, having a quiet, dedicated reading time is crucial. Put your phone in another room and dedicate a specific 20 minutes daily for reading (e.g., right before bed or during a transit commute).

●          Utilize UTM resources: As a student paying tuition fees, it is wasteful not to fully utilize UTM library resources, including its digital collections. UTM’s library offers vast e-book and digital database collections accessible right from our smartphones.

So, here is the final nudge: notes are fantastic for acing exams, but books are the actual fuel that prepares students for the marathon of the real world. I challenge you (my students especially), just read one book this week! Go visit the library (physical or digital.  No excuses!) and help me happily update my current “research findings” on student borrowing habits.

On a completely unrelated note (wink, wink): after a decade of teaching undergraduate educational psychology, I am still waiting for the first student to casually drop a reference to B.F. Skinner’s books. Seriously, please break my personal observation streak! Don’t let me think the behaviourists were right about a lack of reinforcement! Oh no… maybe they are still correct after all!

How to Write a Problem Statement

Disclaimer: Most students think a Problem Statement is just a place to complain that ‘not enough people have studied this.’ Newsflash: Just because no one has studied the habits of feeding stray cats in the faculty parking lot doesn’t mean it’s a ‘research gap’ worth a degree.

To save you from the ‘Major Correction’ heartbreak, I’ve compiled some tips based on comments from reviewing actual drafts from your peers. Whether you’re looking at AI, phubbing, or inclusion for students with special educational needs, remember: if your problem statement doesn’t make the reader feel like the world is slightly on fire, you’re just writing a book report. Read on to learn how to turn your ‘I’m interested in this’ into a ‘The world needs this study right now“. Or is it? (Tongue in cheek remark!)

1. The Ideal (The “Should Be”)

Start by stating how the world should work based on theory or policy or established findings (you need to exemplify this through your literature search). This is your Vision.

  • Example: “In an ideal inclusive classroom, the physical presence of students with disabilities should naturally foster empathy in their peers.”

What can be understood from one sentence? What can be improved? Where is the policy that emphasis the scenario? Look at the following example:

Theoretically, the integration of Students with Special Educational Needs (SEN) into mainstream classrooms is predicated on the assumption that daily contact will instinctively cultivate empathy among typically developing peers (Author, Year). A main emphasis of inclusive education is based on contact theory, which posits that the mere physical proximity of marginalized groups serves as a primary catalyst for developing affective empathy and prosocial behaviors among students (Author, Year). While global inclusive education frameworks are designed under the ideal that physical inclusion naturally fosters social empathy, empirical evidence suggests that mere presence does not automatically translate into meaningful relational engagement (Author, Year).

2. The Reality (The “But”)

Describe the current situation, supported by recent statistics or observations. This is where you introduce the Tension. You can add Key Phrase to connect and zoom in more into your research “problem”. This is your interest/intention.

  • Example: “However, in Malaysia’s highly competitive, achievement-oriented system, students often prioritize grades over peer connection. (Add Key Phrase) In practice, however, [Statistics/Observation] suggest that…”

3. The Gap (The “Missing Link”)

This is the heart of your research. What is missing from our current knowledge? Is it a theoretical gap, a methodological gap, or a contextual gap (e.g., it’s been studied in the West, but not in Malaysia)? This is the “solution”.

  • Example: “While many research showed students are avoiding school counselors, in the context of Malaysian education system, the concern is whether this is due to cognitive stigma or a lack of relational trust. (Add Key Phrase) Despite these trends, there is a critical lack of empirical evidence regarding…”

4. The Consequence (The “So What?”)

Explain the “cost of ignorance.” If we don’t do this study, what bad things will happen?

  • Example: “Without understanding this relationship, clinical interventions will continue to fail, leaving a generation of students without professional mental health support. (Add Key Phrase) Unless this gap is addressed, [Stakeholders] will continue to struggle with…”

The “Problem Statement” Checklist

As a self-check, go to your draft of Problem Statement and look at these five criteria:

  1. Is it a Problem or a Topic? (Example to differentiate a topic and a problem. A topic is “Self-compassion.” A problem is “Trauma survivors cannot practice self-compassion because they lack an internalized caregiver.”)
  2. Is there a Tension? (Does it show two ideas clashing, like “Digital tools for learning” vs. “Digital dependency”?)
  3. Is it Grounded? (Do you have citations from the last 2–5 years to prove the problem is current?)
  4. Is it Specific? (Avoid words like “Many people” or “A lot of.” Use “75% of students” or “Postgraduate researchers in Malaysia.”)
  5. Does it lead to your Research Questions? (The RQs should feel like the only logical next step after reading the problem.)

Quick Tips for Success

  • Avoid the “No Study” Argument: Don’t just say “No one has studied this.” Instead, say “Because this has not been widely studied, it is important to explore how people make policy decisions without evidence.”
  • Use a Theoretical Bridge: Mention a theory (like Attachment Theory or Contact Theory) to show that your problem is not just an opinion but it is a scientific inquiry.
  • Watch Your Tone: Don’t be too emotional. Use objective, academic language. Instead of saying “It is a heartbreaking tragedy,” say “This represents a significant psychosocial challenge.”

A good problem statement doesn’t just tell your readers what you want to study. It tells them why the world is currently ‘broken’ and how your study provides the blueprint to fix it.

Is it wrong to use convenience sampling and quota sampling?

There is no absolute right or wrong in using convenience sampling and quota sampling. However, in rigorous educational research, they are often discouraged because they introduce systematic weaknesses that can undermine the credibility and defensibility of findings.

Here is the precise rationale:

1. Threat to Representativeness (External Validity)

  • Convenience sampling selects participants based on ease of access (e.g., your own students).
    • In action research, convenience sampling is usually the default because a teacher is studying the environment where he/she teaches already in. Thus, it is appropriate because action research is participatory. A teacher is the “insider” researcher, and the participants are the students or colleagues directly involved in the process one wants to improve.
    • However, a teacher cum researcher who is using action research can move from selecting participants through convenience sampling to purposive sampling. So, it is more suitable because it is often seen as more “rigorous” in action research because it ensures the data comes from the people most affected by the issue.
    • Example: A lecturer wants to explore his/her supervision in terms of its effectiveness and enhancing students’ learning experience. So, even though students who are under his/her supervision is convenience sample, yet the lecturer cum researcher can use criterion Sampling when he/she focuses only on specific criteria such as his/her research samples are students who do not have any background in education who are doing PhD in Education. In this regard, criterion sampling as a type of purposive sampling is chosen because the lecturer cum supervisor cum researcher selects participants because they meet a specific predetermined criterion and thus, it shows a more deliberate research design. It moves the study from “I just used who was there” to “I strategically chose these participants because they have the specific experience needed to solve the research problem.“
  • Quota sampling ensures proportions (e.g., gender, age) but still relies on non-random selection. While the primary strength of quota sampling is its ability to mirror the population’s known distribution of certain characteristics (or controlled characteristics), ensuring these proportions (e.g., gender, age, location) are reflected in the sample, yet it may not be representative of the population for other uncontrolled characteristics (e.g., income, specific attitudes). Since selection within quotas is non-random, certain segments of a quota group might be systematically under- or over-represented.

The issue:
Neither method (convenience and quota sampling) gives every member of the population an equal chance of selection. This leads to sampling bias, meaning the sample may not reflect the actual population.

Consequence: Findings cannot be confidently generalised to the wider educational context.

Note: For action research, findings are not meant for generalisation and thus, using convenience sampling or purposive sampling suits with its nature. Action research, by its very nature, is a localized and context-specific form of inquiry aimed at solving immediate, practical problems and implementing improvements within a particular setting (such as a classroom, school, or organization). Consequently, the findings generated from an action research study are typically not intended or suitable for broad generalization to other populations or settings. Therefore, employing non-probability sampling techniques, such as convenience sampling or purposive sampling, is entirely consistent with the core principles and aims of action research.

2. High Risk of Systematic Bias

These methods are particularly vulnerable to:

  • Selection bias (researcher chooses who is “available” or “fits”)
  • Volunteer bias (participants who agree may differ systematically)
  • Context bias (e.g., one class, one school culture)

Note: In education, this is critical because student performance, motivation, or behaviour can vary widely across contexts (schools, regions, SES).

3. Weak Alignment with Inferential Statistics

In quantitative studies, especially those aiming for hypothesis testing, prediction and generalisation, sampling design must support statistical assumptions. Thus, with convenience or quota sampling:

  • You cannot justify probability-based inference
  • Statistical conclusions become methodologically fragile

4. Limited Transferability (Qualitative Context)

Even in qualitative research, convenience sampling often produces shallow or homogeneous data and it may not capture information-rich cases. This weakens, the depth of analysis, conceptual development and credibility of interpretations

5. Methodological Inconsistency

Sampling must align with research intent:

  • If your goal is theory generation -> you need purposeful/theoretical sampling
  • If your goal is generalisation -> you need probability sampling

Using convenience or quota sampling often signals a mismatch between research design and sampling logic.

What is the exception of using convenience sampling?

1.Preliminary Research

Used to generate preliminary insights or hypotheses.

Example: A psychology lecturer in Malaysia surveying her own students to test whether a new questionnaire is understandable before wider use. This is in the stage of refining research instrument (developmental stage of a research instrument), prior to pilot study. This exploration phase, not the actual data collection and can be done in several cycles of research instrument refinement.

2. Pilot Studies / Feasibility Testing

Helps refine research tools, methods, or logistics before committing resources to a full-scale study.

Example: A research student is studying about digital usage among postgraduate university students and plan to conduct the data collection using online mode. The research student wants to test whether an online survey platform works smoothly by first distributing it to postgraduate classmates or fellow research colleagues.

3. Resource-Constrained Situations

When time, budget, or access limitations prevent random sampling.

Example: A small NGO collecting quick feedback from nearby communities due to limited funding about the effectiveness of their services which the communities receive and use.

4. Classroom or Informal Research

Used in teaching, training, or internal assessments where generalizability is not required.

Example: A statistics class at a university using classmates as a sample to practice survey analysis. It acknowledges the practical reality of being a practitioner-researcher.

What you must do (critical for assessment)

If you use convenience sampling, you must demonstrate methodological awareness:

1. Justify the choice

Explain clearly:

  • Why this sample is accessible and relevant
  • Why alternative sampling methods were not feasible

Example: “Convenience sampling was employed due to accessibility to a defined cohort within the institution, allowing timely data collection within the study period.”

2. Acknowledge limitations

Be explicit about:

  • Limited representativeness
  • Restricted generalisability
  • Potential sampling bias

3. Align with research design

Ensure consistency:

  • If quantitative research -> avoid strong claims of population generalisation
  • If qualitative research -> emphasise contextual depth, not representativeness

Cara Penulisan Objektif Pembelajaran

1. Hubung kaitkan Standard Pembelajaran (SP) dengan Objektif Pembelajaran (OP)

Objektif Pembelajaran mesti berpaksikan kepada Standard Pembelajaran (SP). SP menentukan aras taksonomi yang perlu dicapai oleh murid, manakala OP memperincikan hasil pembelajaran yang diharapkan pada akhir sesi.

  • Contoh SP: “Murid boleh menyenarai kepentingan reka bentuk dan teknologi.” -> Menunjukkan Aras 1 (Mengingat) dalam Taksonomi Bloom.
  • Saranan OP: OP boleh ditulis pada aras yang sama atau dinaikkan ke Aras 2 (Memahami) untuk memperkukuh kefahaman murid.
    • Formula OP “Siapa + Kata Kerja + Apa + Bagaimana”: “Pada akhir sesi pengajaran dan pembelajaran, [Murid] dapat/boleh [Kata Kerja Aras Taksonomi] + [Topik/Kandungan] + [Ukuran/Kriteria].”
    • Contoh OP (selaras dengan SP): Murid boleh menjelaskan kepentingan reka bentuk dan teknologi dalam kehidupan harian.

2. Hubung kaitkan Objektif Pembelajaran (OP) dengan Kriteria Kejayaan (KK)

OP memperincikan hasil pembelajaran yang diharapkan pada akhir sesi dan KK menjelaskan kualiti objektif pembelajaran yang ingin dicapai melalui pembuktian pencapaian murid.

Formula KK: [Murid] berjaya jika dapat/boleh [Kata Kerja/Tindakan] + [Bilangan/Kuantiti] + [Topik/Hasil] + [Ukuran/Kriteria] dengan [Tahap Ketepatan/Kualiti].”

Contoh KK (selaras dengan OP): Murid berjaya jika dapat/boleh menjelaskan lima (5) kepentingan reka bentuk dan teknologi dalam kehidupan harian dengan tepat.

Beberapa contoh Objektif Pembelajaran dan Kriteria Kejayaan mengikut Aras Taksonomi Kognitif Bloom

Aras 1: Mengingat (Remembering)

  • Objektif Pembelajaran (OP): Pada akhir sesi pengajaran dan pembelajaran, murid dapat menyenaraikan alatan tangan yang digunakan dalam proses pembuatan projek yang dirancang.
  • Kriteria Kejayaan (KK): Murid berjaya jika dapat menyatakan sekurang-kurangnya 3 alatan tangan dengan fungsinya secara ringkas dengan tepat.

Aras 2: Memahami (Understanding)

  • Objektif Pembelajaran (OP): Pada akhir sesi pengajaran dan pembelajaran, murid dapat menerangkan fungsi reka bentuk konvensional dan reka bentuk moden secara bertulis.
  • Kriteria Kejayaan (KK): Murid berjaya jika dapat menjelaskan 2 fungsi utama dari segi bahan dan teknologi yang digunakan dalam kedua-dua reka bentuk tersebut dengan jelas.

Aras 3: Mengaplikasi (Applying)

  • Objektif Pembelajaran (OP): Pada akhir sesi pengajaran dan pembelajaran, murid dapat mengira ukuran satu lakaran perspektif satu titik lenyap bagi satu bongkah geometri mengikut teknik yang betul.
  • Kriteria Kejayaan (KK): Murid berjaya jika dapat mengira ukuran 1 lakaran perspektif yang mempunyai titik lenyap, garisan ufuk, dan garisan unjuran yang tepat.

Aras 4: Menganalisis (Analyzing)

  • Objektif Pembelajaran (OP): Pada akhir sesi pengajaran dan pembelajaran, murid dapat membezakan komponen-komponen elektrik yang terdapat dalam sebuah litar berfungsi kepada bahagian-bahagian kecil.
  • Kriteria Kejayaan (KK): Murid berjaya jika dapat membezakan 4 komponen utama (sumber, suis, konduktor, beban) dalam gambar rajah litar dengan tepat.

Aras 5: Menilai (Evaluating)

  • Objektif Pembelajaran (OP): Pada akhir sesi pengajaran dan pembelajaran, murid dapat membuat justifikasi pemilihan bahan kitar semula yang paling sesuai untuk membina model produk.
  • Kriteria Kejayaan (KK): Murid berjaya jika dapat memberikan 2 alasan yang munasabah (seperti kos, ketahanan, atau estetika) mengapa bahan tersebut dipilih berbanding bahan lain dengan jelas.

Aras 6: Mencipta (Creating)

  • Objektif Pembelajaran (OP): Pada akhir sesi pengajaran dan pembelajaran, murid dapat mereka cipta satu gajet elektrik yang berfungsi menggunakan bahan kitar semula secara kreatif.
  • Kriteria Kejayaan (KK): Murid berjaya jika dapat membina 1 model gajet yang berfungsi sepenuhnya (lampu menyala/motor bergerak) dengan kemasan yang kemas.

Sampling Design

In educational research, sampling design adheres to the same foundational logic as general research methodology, but it is contextualised around educational populations such as students, teachers, school leaders, or institutions. It is not a procedural afterthought; rather, it is a methodological decision that must be explicitly aligned with the research problem, research questions, and overall research design.

Sampling design serves several key functions:

  • It determines who or what constitutes the source of data (e.g., learners, classrooms, schools).
  • It ensures the appropriateness and credibility of the findings.
  • It enables the researcher to operationalise the intent of the study whether that intent is to generalise, explain, explore, or generate theory.

Crucially, sampling design must be coherent with the research design. Different research designs imply different logics of sampling:

  • In quantitative designs (e.g., experimental, survey), sampling strategies such as probability sampling (e.g., simple random, stratified) are typically employed to enhance representativeness and generalisability.
  • In qualitative designs, sampling is usually purposeful and criterion-based, focusing on depth, richness, and relevance of data rather than representativeness.

For example, if the research aim is to develop a theory that explains an educational phenomenon, a Grounded Theory design is appropriate. In this case, theoretical sampling is used because participants are selected iteratively based on their potential to contribute to the emerging theory. Sampling decisions are made continuously throughout the study, guided by the evolving analysis rather than predetermined at the outset.

In short, an effective sampling design in education is not chosen in isolation as it is logically derived from the research purpose and tightly integrated with the research design, ensuring methodological congruence and the production of meaningful, defensible findings.

The process typically involves defining three levels:

  • Population: The broad group of interest sharing a characteristic (e.g., all primary and secondary teachers in a country, or all undergraduate students).
  • Target Population: The specific subset you can reasonably identify and study (e.g., primary teachers with a specific certification, or undergraduates at three specific universities).
  • Sample: The actual individuals from that target population who are selected to participate in your study.

Educational research often relies heavily on non-probability sampling because getting a complete, randomized list of all students or teachers (a sampling frame) is rarely feasible. For example, researchers might use convenience sampling by distributing a questionnaire in a specific class or teacher’s social media group via WhatsApp or Telegram group. Alternatively, they might use purposive extreme case sampling to study specific subgroups, like selecting only Dean’s List students to understand high-achievement study habits.

Sampling design is the overarching framework of your sampling process. While a “sampling scheme” is the specific technique used to select units (like the river sampling – which is one types of convenience sampling), a sampling design includes the entire structure: the number and types of schemes, the sample size, and the relationship between participants.

An effective sampling design involves three main pillars:

1. The Sampling Scheme (The “How”)

This is the method used to select your sample from the population. It generally falls into two categories:

  1. Probability Sampling: Every member has a known and equal chance of being selected (e.g., simple random or stratified sampling). This is ideal for statistical generalization.
  • Non-Probability Sampling: Selection is based on accessibility or specific criteria (e.g., convenience, river, or purposeful sampling). This is used when a full list of the population is unavailable.

2. Sample Size (The “How Many”)

This determines the statistical power and the depth of your data.

Some Examples of Non-Randomised Sampling (Not Purposive Sampling)

1. Convenience Sampling

The researcher selects participants who are the easiest to reach or most available. It is often used for pilot testing or when resources are limited.

  • Example 1 for convenience sampling: A university lecturer surveying students in their own classroom to get quick feedback on a new teaching tool. It is appropriate to use convenience sampling, but it is often better to use purposive sampling.
    • Convenience Sampling: You choose participants because they are easy to reach (e.g., “I am teaching this class, so I will study this class”).
    • Purposive Sampling: You choose participants because they fit the purpose of the study (e.g., “I am studying my class on how AI helps struggling writers but I will specifically select the 5 students in my class who have the lowest writing scores“).
  • Example 2 for convenience sampling: A company representative standing in a shopping mall and asking passersby for their opinion on a new product.

2. Quota (Ratio) Sampling

While “ratio” is often used in stratified sampling (one type of probability sampling) in random context, in a non-random context, it usually refers to Quota Sampling. For quota sampling, the researcher ensures the sample reflects specific proportions (ratios) of certain traits in the population (e.g., 50% male, 50% female).

Example: A researcher wants to study students satisfaction of using university library. If they know the university has the ratio of 60% female and 40% male users/students, they recruit exactly 60 female students and 40 male students from the library lobby or library user log until those “quotas” are filled.

3. Snowball Sampling

Used when the target population is “hidden” or difficult to locate. Existing participants recruit or refer others from their social circle who meet the criteria.

Example: A study on the experiences of university students using neuroenhancer drugs. The researcher finds one participant, who then introduces them to others in their “community”.

Some Examples of Non-Randomised Sampling (Purposive Sampling Techniques)

1. Theoretical Sampling

It is a qualitative sampling strategy where you select new participants or data sources based on the interpretative theory that is emerging from your ongoing data analysis. It is a specific type of purposive (or purposeful) sampling. It is the principal sampling method used in the grounded theory approach. Instead of aiming to simply increase the overall sample size, the goal is to collect specific data that helps develop emergent themes, assess their relevance, and refine your concepts.

Example: A researcher studying “burnout” starts by interviewing medical students until reaching theoretical saturation (the point where concepts are dense, relationships are stable, and new data no longer adds explanatory power). After discovering that “lack of support” is a key theme, they specifically seek out medical students who work in high-support environments during their internship to see if the theory holds.

2. Deviant (Extreme) Case Sampling

This method focuses on “outliers” or cases that are unusual, highly successful, or notable failures. It is a specific type of purposive (or purposeful) sampling. Studying these extremes can provide unique insights that “average” cases cannot.

Example: A study on “academic success” that specifically interviews students with the highest possible GPAs and students who have dropped out, rather than those with average grades.

3. Intensity sampling

It is a purposive, non-random sampling strategy used to select cases that manifest the phenomenon of interest intensely, but not extremely. In qualitative research, while “Deviant Case” sampling looks at the outliers (the absolute best or worst), Intensity Sampling focuses on “rich” cases that are excellent examples of the situation of being so unusual that they no longer represent the general population.

Example: A study on the perceived impact of case-based learning on student engagement, researchers would not interview a student who hates school or a student who is a genius. Instead, they would select students who are consistently engaged and vocal in class. They provide “intense” data about how the method works because they are actively experiencing it, but they are still “normal” students.

4. Criterion Sampling

It is a specific type of purposive (or purposeful) sampling. It involves actively selecting participants who meet a predetermined, important criterion because that characteristic makes them “information-rich” regarding your research topic

Example: Selecting only individuals who have used a specific software for more than 10 hours a week for a user experience study.

5. Typical case sampling

It involves selecting participants who represent what is considered “average” or “normal” to illustrate a standard experience. It is a specific type of purposive (or purposeful) sampling.

Example: If a researcher wanted to study how a new curriculum benefits the average student, they would purposefully select only students with average grades.

6. Maximum Variation Sampling

Maximum variation sampling involves intentionally selecting participants or cases that are as different from one another as possible along a specific dimension. So, it is a specific type of purposive (or purposeful) sampling. While it might seem counterintuitive to look for such extreme differences, the primary goal is actually to identify core, shared patterns that remain consistent despite that high level of diversity. By deliberately expanding the range of variation, researchers can capture the true breadth of a phenomenon and confidently identify common themes that cut across different environments.

Example: A study exploring how teachers adapt to a new digital grading system. Instead of selecting a random group of teachers, the researcher purposefully selects a brand new first-year teacher, a mid-career teacher, and a veteran teacher with 30 years of experience. The goal is to capture as much diversity as possible along the dimension of “teaching experience.” If the researcher finds that all three of these vastly different teachers struggle with the exact same software issue, that finding is very strong because it represents a shared pattern that cuts across their differences.

7. Expert Sampling

Expert sampling is a type of purposive sampling where a researcher specifically select participants who possess a high degree of specialized knowledge or expertise in a study area. It is commonly used in the early stages of research to help shape the research questions and the study’s design.

Example: To study problematic mobile social media use among students, a researcher would not use expert sampling to select the undergraduate participants. However, a researcher might use it to select a small group of psychology professors or media researchers to review and validate the survey questionnaire before distributing it for data collection.

Creativity

Creativity is a complex cognitive process defined as the ability to generate new, original, and valuable ideas by combining existing knowledge in novel ways. It is often described as “thinking in new ways to make something original and useful”. Creativity is not just about having a single “good idea”. It involves four specific cognitive dimensions:

  • Originality: The ability to produce an idea that is new or different from the usual. For example, a student writing a poem using fresh, unique metaphors.
  • Fluency: The capacity to generate a large number of ideas or potential solutions for a single problem. An example would be brainstorming ten different ways to reduce plastic waste.
  • Flexibility: The ability to see problems from multiple viewpoints or different perspectives. For example, designing a product that is equally functional for both right- and left-handed users.
  • Elaboration: The process of adding details to an idea or refining it to make it more complete. This could involve taking an initial rough sketch and turning it into a detailed prototype.

The Nature of Creative Thinking

There are few important distinctions regarding how creativity relates to intelligence and thought patterns:

  • Creativity vs. IQ: While highly creative people often have high IQs, having a high IQ does not necessarily mean a person will be creative.
  • Divergent Thinking: Creativity is closely linked to divergent thinking, which is the ability to explore many possible solutions to a problem.

Promoting Creativity in the Classroom

To encourage students to think creatively, the materials suggest that instructors should:

  • Create a Safe Environment: Establish a learning space where students feel safe to take risks and share unusual ideas.
  • Support Autonomy: Provide a learning environment that supports student independence.
  • Model and Value: Demonstrate creative thinking personally and show students that original ideas are valued.
  • Allow Time: Give students dedicated time to engage in the creative process rather than rushing toward a single “correct” answer

Critical Thinking

Critical thinking is one type of Complex Cognitive Processes.

1. Core Elements of Critical Thinking

According to Nickerson (1988), engaging in critical thinking requires four essential components:

  • Motivation: The drive or desire to think deeply about a subject.
  • Knowledge: Having some existing information or background about the issue being considered.
  • Metacognition: Being aware of and monitoring your own thought processes.
  • Component Skills: A specific set of skills used to process information.

2. Five Key Features of Critical Thinking

Critical thinking has five distinct features, each with practical classroom applications:

  1. Analysis: Breaking information into smaller parts to understand how they relate to one another.
    • Example: Analyzing the different causes of World War II in a History class.
  2. Evaluation: Assessing the credibility, logic, and evidence behind an argument or data set.
    • Example: Evaluating whether experimental data in Science actually supports a specific hypothesis.
  3. Inference: Drawing logical conclusions based on the information that is currently available.
    • Example: Inferring a character’s motives in an English literature assignment based on their dialogue.
  4. Explanation: The ability to justify your reasoning or viewpoint clearly to others.
    • Example: Explaining why honesty is the best course of action in a Moral Education case study.
  5. Reflection (Metacognition): Thinking about your own thought process and how you reached a specific decision.
    • Example: Reflecting on how you reached a conclusion during a group discussion.

3. Promoting Critical Thinking in the Classroom

Students are unlikely to engage in critical thinking spontaneously. Therefore, specific tasks are more effective than others at encouraging this process:

  • High-Impact Tasks: Comparing two different solutions and deciding which is more effective promotes critical thinking because it requires evaluation and analysis.
  • Low-Impact Tasks: Memorizing definitions, listening to a lecture, or completing a basic multiple-choice quiz do not typically encourage critical thinking.

Metacognition

The Two Components of Metacognition

Another type of complex cognitive processes, is metacognition. Metacognition is divided into two primary parts that work together to enhance learning:

1. Knowledge of Cognition (Metacognitive Knowledge)

This refers to knowing what you know and understanding how you learn. This information is stored in your long-term memory and consists of three types of knowledge: declarative, procedural, and conditional.

2. Control of Cognition (Metacognitive Regulation)

This is the active management of your knowledge to learn effectively. It involves three essential skills:

  • Planning: Setting goals and choosing strategies before starting a task (e.g., skimming headings before reading).
  • Monitoring: Checking your progress and comprehension while learning (e.g., asking yourself if you truly understand a theory).
  • Evaluating: Reflecting after learning to judge the effectiveness of your strategies (e.g., deciding if summarizing helped you understand an article).

Why Metacognition Matters

Developing these skills is crucial because it:

  • Improves self-regulated learning.
  • Enhances problem-solving and critical thinking.
  • Helps learners transfer knowledge to new situations.
  • Encourages lifelong learning by teaching students how to learn.

Teachers can promote this awareness by using strategies like note-taking, summarizing, and the SQ4R method (Survey, Question, Read, Recite, Review, and Reflect).

Why Metacognition is “Complex”

Metacognition is considered a higher-order skill for several reasons:

  • Beyond Simple Recall: While simple processes involve basic activities like attention and memory , metacognition involves the active regulation of these processes.
  • Knowledge Application: It requires the learner to apply declarative, procedural, and conditional knowledge to manage their own learning.
  • Executive Control: It involves complex regulatory skills such as planning (choosing strategies), monitoring (checking comprehension), and evaluating (judging effectiveness).
  • Interconnectivity: In the Bloom’s Taxonomy frameworks, metacognitive-related tasks like Evaluating and Synthesizing are placed at the highest levels of cognitive objectives.

Relationship to Other Complex Cognitive Processes

Metacognition acts as a “support system” for other complex cognitive activities:

  • Problem Solving: Helps learners monitor which strategies are working and when to switch approaches.
  • Critical Thinking: One of the five key features of critical thinking is Reflection, which is specifically defined as metacognition.
  • Transfer of Learning: Strong metacognitive skills are a significant factor in a student’s ability to successfully transfer knowledge to new contexts.
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