Evidence-Based Study Tips for College Students

March 30, 2026 For Students

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A Short Guide to Studying Effectively in College: How to Hack Your Brain for Better Learning

If you are like many college students, your primary study strategies probably consist of rereading your textbooks or lecture slides, highlighting key passages, and cramming the day before an exam. You might feel highly productive during these long study sessions, but the science of learning tells a different story: rereading text and massed practice (cramming) are by far the least productive study strategies(Dunlosky et al. 2013). These common methods merely boost short-term retrieval strength (Bjork and Bjork 1992; Benjamin et al. 1998) and create an “illusion of knowing” (Glenberg et al. 1982). When you read a text multiple times, the material becomes familiar, and you mistake this perceptual fluency for true mastery of the subject. To build deep, durable knowledge, you need to embrace “desirable difficulties”—short-term impediments that make learning feel harder and slower, but ultimately trigger cognitive processes that optimize long-term retention and the transfer of skills (Bjork 1994; Bjork and Bjork 2011).

This post is intended to teach you actionable, research-backed study tips on how to learn effectively, with specific examples for computer science students to enable everyone to get the most out of their college experience! Interestingly, despite actually measurably learning more effectively, learners who study based on these techniques often subjectively believe they have learned less effectively, because short-term performance is often perceived to be worse due to the additional struggle of desirable difficulties (Kornell and Bjork 2008; Simon and Bjork 2001). This illustrates that the perception of learning is not always aligned with the actual learning. Actual learning is the permanent change in knowledge, understanding, or high-order skills that affects your future ability to perform well. What you observe is your past performance. The study tips in this post are empirically validated to be effective and should help you pick learning strategies that actually work rather than just feel like they work.

Fun fact: a significant portion of the research presented in this post was actually done at UCLA by the Bjork Learning and Forgetting Lab!

Desirable Difficulties: Embrace the Struggle

It is a common misconception that if you are learning effectively, the process should feel smooth, fast, and easy. In reality, the exact opposite is true! When learning is completely effortless, it is almost always superficial and easily forgotten. To build truly durable knowledge, you have to lean into “desirable difficulties“—intentional roadblocks that force your brain to work harder to process, connect, and retrieve information (Bjork 1994; Bjork and Bjork 2011). For example, instead of looking something up in your notes or textbook, you should always try to recall it yourself or actively construct an answer to a question yourself, even if you are not sure it is correct. The mental sweat you experience when you can’t quite remember the time complexity of an algorithm, or the frustration of grappling with a complex concept, isn’t a sign that you aren’t smart enough. Rather, that struggle is the biological mechanism of your brain building stronger, more permanent neural pathways. Simply put: effective learning is effortful (Brown et al. 2014). However, note that not all difficulties are desirable. For example, studying while you are sleep-deprived, hungry, sick, or highly anxious; reading the material in an unfamiliar language; or studying a topic without pre-required fundamentals are undesirable difficulties and they should be reduced. Only difficulties that present challenges to the learner but are not of such difficulty that the learner cannot eventually meet or overcome them are desirable. A rule of thumb is that what makes your brain do more active work while still reaching an answer is helpful.

• Actionable Advice: When your study routine starts to feel too easy or comfortable, try to add in some challenges that make it harder for yourself. If you are breezing through your flashcards, shuffle them to remove predictable patterns, or force yourself to explain the core concepts out loud to an empty room without looking at your notes. Change the place where you study instead of returning to the same spot. When you feel that cognitive friction, remind yourself that the struggle is a necessary part of the growth process.
• Computer Science Examples: When working on a coding assignment, it is incredibly tempting to rely on AI auto-complete tools (like GitHub Copilot, Cursor, or Codex) or to copy-paste boilerplate code directly from a tutorial or online resource. While this is efficient for workplace production, it bypasses the cognitive effort required for actual learning. To create a desirable difficulty, turn off your AI assistants and force yourself to type out the syntax and build the logic step-by-step from scratch. The mental challenge you feel when you have to manually track down a missing bracket or debug a loop is exactly the kind of effortful struggle that ensures you will be able to catch bugs more quickly tomorrow. Embrace challenges as welcome learning opportunities!

Retrieval Practice: Ditch the Highlighter and Quiz Yourself

One of the most powerful learning tools at your disposal is “active retrieval” (Dunlosky et al. 2013). As a computer scientist, you might think that recalling memory does not have any side effects. While this might be true for most digital storage media (unless they use caching or log requests), it is not true for human memory. In fact, the act of retrieving information from human memory makes that information much easier to access in the future (Roediger and Karpicke 2006). The more often you try to recall information, the easier it becomes to retrieve it in the future. A single, simple quiz after reading a text produces better learning and remembering than reading the text multiple times. Passively re-consuming the material does not have a similar effect as active retrieval (Brown et al. 2014). You have to recall the answers yourself, without looking at the material. Additionally, while simultaneously reinforcing this memory to move it towards more durable storage, retrieval practice also tells you exactly what you know and what you don’t. Also consider that study methods in which you produce an answer yourself, even if it’s purely recall, is more effective than if you just need to recognize a correct answer. For example, flash cards and open-ended questions are often more effective than multiple choice questions, because you have to produce the entire answer yourself rather than just identifying answers are correct or incorrect.

• Actionable Advice: Instead of rereading your notes, lecture slides, or books, put them away and try to write out everything you can remember from the lecture. Create flashcards to quiz yourself on important concepts. Treat practice tests as real tests, forcing yourself to generate the answers without notes rather than just looking at them and nodding. Write a summary of each lecture at the end purely relying on your own memory. Right before the next lecture, recall what you’ve learned in the previous lecture, again purely from your own memory. Use your own memory as often as possible while relying as little as possible on external resources. Favor active production and recall over pure recognition of correct answers.
• Computer Science Examples: When you are starting to write a complex loop or pointer arithmetic, try to recall the syntax from memory instead of looking it up. When you are learning version control with Git, instead of looking up the documentation of each command before you use it, try to recall it from memory. When you are writing a sort function, try to remember all sorting algorithms you have learned and how they are different. At every possible opportunity, try to use your own memory before consulting other resources.

Spaced Practice: Space Out Your Study Sessions

Cramming all your studying into one massive session might help you pass the mid-term the next day, but that knowledge will melt away for the final. Massed practice feels very productive in the moment, but it actively harms long-term retention. To build durable memory, you must space out your practice (Cepeda et al. 2006; Dunlosky et al. 2013). Studying a little bit every week is much more effective than studying for the cumulative duration of these individual study sessions within one single day. Spacing allows time for your memories to consolidate—a process where memory traces are stabilized, given meaning, and connected to prior knowledge (Brown et al. 2014). A study session is more effective after you start to forget a little bit of the material (Brown et al. 2014). Repeating this process of incrementally studying the same material over increasing intervals of time is one of the most effective ways to build durable memory, because it feels more effortful and signals to your brain that the information is important and needs to be retained long-term (Brown et al. 2014).

• Actionable Advice: Set aside time every week to practice on both the current week’s material and topics covered earlier in the quarter/semester. Let a little forgetting set in between study sessions. When you feel a bit rusty, the mental effort required to “reload” the information from long-term memory triggers reconsolidation, which deepens the learning. If you identify missing gaps that you can’t answer by consulting your notes, you can then discuss these in office hours. Your professor or TA will be happy to help you, especially if you are asking questions in a week other than the crowded office hours right before the exam. To help you keep track of your planned spacing sessions, add them to your calendar. To make them more fun and engaging, study together with other students. Study groups are a great way to keep motivated, to express your thoughts verbally, and to learn from each other’s unique experience and perspectives.
• Computer Science Examples: If your class has a project that is due at the end of the term, do not follow the temptation to do most of the work in the last few weeks. Continuously apply the techniques taught in the course every week and make incremental progress. If your course covered more theoretical concepts, periodically revisit older concepts. For example, if you learned about Big-O notation in week one, test yourself on it again in week four, even if it is not on the homework. The effort to retrieve older concepts ensures they stay accessible.

Interleaving: Mix Up Your Problem Types

Most textbooks and many courses use “blocked practice”: They first cover one topic entirely, then the second topic, then the third. The problem with blocked practice is that it never teaches you when to apply a specific solution. In contrast, interleaving mixes the practice of different but related topics or skills. This helps you develop a broader understanding of the relationships between conditions and improve your ability to discriminate between problem types (Kornell and Bjork 2008; Rohrer and Taylor 2007; Dunlosky et al. 2013). This often makes students feel less confident in their knowledge, because they start working on the next topic before they feel they fully grasped the first one. But it is actually more effective for effective learning, because it naturally builds in spaced retrieval practice and it also makes you pay more attention to the connections, similarities, and differences between topics, which is more important for real-world application of the taught skills (Brown et al. 2014).

• Actionable Advice: Once you understand a new problem type, scatter it throughout your practice sequence so that you are alternating between different problems that call for different solutions. For related problems or related topics, mix them up as much as possible. Interleave study sessions between different courses. When quizzing yourself with flash cards or other quizzes, mix different course topics in your deck rather than ordering them by topic.
• Computer Science Examples: If you are studying sorting algorithms, do not do ten practice problems on Merge Sort, followed by ten on Quick Sort, and ten on Bubble Sort. Shuffle the problems randomly. By mixing them up, you force your brain to analyze the dataset and the constraints of the problem to determine which algorithm is the most efficient choice before you write the code. For personal projects or programming study sessions, vary the programming languages you are using. Study topics from different courses together rather than separately. For example, when you are taking operating systems and programming languages together, consider how the choice of programming language impacts the design of operating systems. By mixing up your problem types, you force your brain to contextualize and connect knowledge and achieve deeper learning.

Generation: Struggle First, Check the Solution Later

The act of trying to answer a question or solve a problem before being shown how to do it is known as generation. Even if you make errors in your attempt, wrestling with the problem makes your mind far more receptive to the correct solution when it is finally provided (Jacoby 1978). Unsuccessful attempts at a solution encourage deep processing of the answer and create fertile ground for its encoding.

• Actionable Advice: Try to solve homework problems before you go to the lecture where the solution will be taught. View errors not as failures, but as vital diagnostic information that helps you adjust your strategies.
• Computer Science Examples: If your assignment is to traverse a binary search tree, try to write the script entirely on your own before you search for the standard solution on Stack Overflow or in your textbook. When your code throws an exception you’ve never seen before, try to understand what it means and how to fix it before looking for a quickfix. When you finally review the correct implementation, the logic will click into place and stick with you much longer. Challenge yourself to create new knowledge before trying to find the answer somewhere else.

Component Skills: Manage Your Cognitive Load

Human cognitive architecture is characterized by a very limited short-term or working memory. When a task is highly complex, trying to learn all aspects of it simultaneously can overwhelm your working memory and impede learning (Sweller 1988). This can result in undesirable difficulties. However, if you break a complex task down and practice component skills in isolation, you can develop mastery of these individual skills before combining them into a coherent whole (Ambrose et al. 2010).

• Actionable Advice: Decompose daunting, too complex tasks into smaller, manageable sub-goals. For a very difficult course, start to study individual skills earlier so that in the week before the exam you can focus on combining component skills rather than learning all of them for the first time, which might overwhelm you. Note that managing load is complementary to the advice on interleaving. Interleaving is useful if and only if the entire work fits into your cognitive load capacity. If you reach a point of exhaustion, you should split up the learning into individual component skills before combining them. Until then, practicing related concepts using interleaving is ideal. The difficulty resulting from interleaving is only desirable if you can overcome it successfully. Once it overloads your working memory, the difficulty becomes undesirable. Also note that based on previous experience with some course topics, some students might have learned some component skills already and therefore might be able to combine them into a coherent whole more easily than others. For some courses you will be able to combine the different topics earlier than others while for other courses you will need to study the topics separately for longer than others. Everyone goes through this struggle at different points in time and for different topics. This is a very common part of the college experience.

• Computer Science Examples: Suppose you are tasked with building a full-stack web application (CS35L students should pay attention to this section). If you try to learn frontend design (HTML/CSS/JS), backend logic (Python/Node.js), and database querying (SQL) all at the exact same time without prior experience with any of these, your cognitive load will be too high to learn them together. Instead, isolate the component skills. Build the static front end first. Then, separately, practice writing database queries on simple examples. Once you have a fluent, automated grasp of these individual pieces, you will have the cognitive capacity to successfully integrate them into a dynamic application. To ensure you have enough time to study each individually, make sure you start early right after each concept was taught in the lectures rather than waiting until you really need it. When a skill is getting too complex for you to handle, you have to break it down into smaller, manageable sub-goals to be studied individually.

Growth Mindset: The Foundation for Desirable Difficulties

Adopting all of the strategies listed above is nearly impossible if you operate with a “fixed mindset”—the belief that your intelligence and abilities are static traits you are simply born with. If you have a fixed mindset, struggling with a new concept feels like terrifying proof that you just aren’t smart enough. However, decades of psychological research demonstrate that a “growth mindset” is far more accurate: your brain is highly plastic, and intellectual abilities are absolutely developed through effort, strategic practice, and perseverance (Dweck 2006). To effectively use desirable difficulties, you have to let go of the need for learning to feel “easy” and instead recognize that mental friction is the physical sensation of your brain growing stronger.

• Actionable Advice: Pay attention to your internal monologue and add the word “yet”. Change “I don’t understand this” to “I don’t understand this yet”. When you hit a roadblock, do not view it as a limit of your natural ability. View it as a cue that you need to adjust your study strategies, increase your effort, or seek out office hours. If you’ve done well in a class, don’t assume you’re a “natural” at the subject. Instead, recognize that your success is the result of effective strategies and consistent effort, which you can replicate in more challenging courses. And if you struggle too much, don’t be afraid to ask for help and visit office hours or email your instructor(s). All of them are always willing to help, especially if you let them know how hard you’ve tried already.
• Computer Science Examples: There is a pervasive and toxic myth in software engineering that some people are just naturally born with a “coding gene”. This is entirely false. When your C program segfaults for the fiftieth time, or you are trying to untangle a messy git rebase, it is easy to feel like an imposter. A fixed mindset tells you to give up because you aren’t cut out for computer science. A growth mindset reminds you that every single expert developer started exactly where you are. Bugs and compiler errors are not indictments of your intelligence. They are simply the mechanism by which you build mastery. You are entirely capable of conquering these complex systems, provided you embrace the resilient, effortful struggle required to learn them and you are not afraid to ask for help when you need it.

Quiz & Conclusion

As you just learned, recalling what you just learned is the best way to form lasting memory. Use these flashcards and the quiz below to test your knowledge and start forming memory via active retrieval. Revisit them every once in a while to further strengthen your memory.

Study Tips Flashcards

Test your knowledge on evidence-based study techniques!

Why is rereading a textbook often an ineffective study strategy?

It creates an ‘illusion of knowing’ or perceptual fluency.

Rereading makes the material feel familiar, but it doesn’t trigger the deep cognitive processing required for long-term retention.

What are ‘Desirable Difficulties’?

Challenges that make learning feel harder and slower but trigger processes that optimize long-term retention.

Examples include retrieval practice, spacing, and interleaving. They force the brain to work harder, which strengthens neural pathways.

What is Retrieval Practice?

The act of actively recalling information from memory.

Instead of looking at notes, you quiz yourself. This reinforces memory traces and identifies gaps in knowledge.

Define ‘Spaced Practice’ (Spacing).

Spreading out study sessions over increasing intervals of time.

Spacing allows for some forgetting to occur, which makes the subsequent retrieval more effortful and effective for durable memory.

What is Interleaving?

Mixing the practice of different but related topics or skills.

Unlike ‘blocked practice’ (focusing on one topic at a time), interleaving helps learners discriminate between problem types and understand relationships.

How does ‘Generation’ improve learning?

Attempting to solve a problem before being shown the solution makes the mind more receptive to the answer.

Even if you make mistakes, the effort of generation creates fertile ground for encoding the correct solution later.

Workout Complete!

Your Score: 0/6

Come back later to improve your recall!

Study Tips Quiz

Test your understanding of the evidence-based study techniques.

After reading a chapter on algorithms three times, a student feels incredibly confident about the upcoming exam. However, they end up failing. According to learning science, what psychological trap did this student likely fall into?

Correct Answer:

Explanation

The text references Bjork & Bjork’s ‘illusion of knowing’, where rereading creates perceptual fluency (familiarity) that students mistake for actual mastery.

According to the science of learning, why should you intentionally make your study sessions feel harder?

Correct Answer:

Explanation

It is incredibly common to mistake the feeling of ‘easy’ studying (like rereading highlighted notes) for actual learning. However, that smooth, effortless feeling usually just means the material is familiar, not that you have mastered it. When you introduce ‘desirable difficulties’—like forcing yourself to recall information without looking—the mental friction you feel is actually the biological process of your brain forging stronger, more permanent neural pathways!

Why do evidence-based study techniques often feel slower, clumsier, and more frustrating to the learner?

Correct Answer:

Explanation

This describes desirable difficulties (Bjork). Effective learning strategies introduce short-term impediments that make learning feel harder, which ultimately triggers processes that optimize long-term retention.

You are working on a new Python project and decide to turn off your AI coding assistant (like GitHub Copilot). According to the concept of ‘desirable difficulties’, what is the primary benefit of this highly frustrating choice?

Correct Answer:

Explanation

Cognitive offloading is the use of external tools (like calculators, calendars, or AI) to reduce the mental effort required for a task. While AI auto-complete tools are fantastic for workplace efficiency, they are terrible for initial learning because they do the heavy cognitive lifting for you. By turning them off, you force yourself to struggle through the logic, manually track down missing brackets, and figure out the syntax from scratch. That exact frustration is what cements the knowledge in your long-term memory, ensuring you won’t need the AI to do it for you on the final exam.

A junior developer wants to master a new web framework. Which of the following approaches represents the most effective memory-strengthening technique?

Correct Answer:

Explanation

This represents retrieval practice. Roediger and Karpicke’s research demonstrates that actively retrieving information from memory (without looking at the source) physically strengthens memory traces far better than passive review.

A project team must pass a rigorous cybersecurity certification in one month. How should they schedule their preparation to ensure the knowledge remains accessible long after the test is over?

Correct Answer:

Explanation

This represents distributed practice (spacing). Cepeda et al. showed that allowing a little forgetting to set in forces the brain to ‘reload’ the information, triggering reconsolidation and deeper learning.

A data structures student is practicing graph algorithms. Instead of doing all the shortest-path problems, followed by all the minimum-spanning-tree problems, she shuffles them together. What specific cognitive capability does this heavily cultivate?

Correct Answer:

Explanation

This represents interleaving. Rohrer and Taylor note that while blocked practice teaches you how to do a problem, interleaving improves your ability to discriminate between problem types and know when to apply specific solutions.

Before attending a lecture on building neural networks, a software engineering student tries to sketch out the math for backpropagation, making several fundamental logic errors. Pedagogically speaking, how should we view this attempt?

Correct Answer:

Explanation

This represents generation. Jacoby’s work shows that struggling with a problem before seeing the solution (even if errors are made) makes the mind far more receptive to the correct answer when provided.

A student is completely overwhelmed trying to combine Git, shell scripting, and learning a new programming language all at the same time. What should they do to optimize their cognitive architecture?

Correct Answer:

Explanation

This represents managing cognitive load. Sweller’s research dictates that working memory is highly limited; complex tasks must be decomposed into component skills so fluency can be achieved without overwhelming the learner.

A student struggles heavily with their first algorithms assignment and decides, ‘I’m just not wired for complex math and logic.’ This reaction is a classic example of:

Correct Answer:

Explanation

This references Carol Dweck’s research on growth mindset. Believing abilities are innate and unchangeable is a fixed mindset, whereas a growth mindset embraces the fact that abilities can be improved through effort.

Which of the following are considered ‘desirable difficulties’? (Select all that apply)

Correct Answers:

Explanation

Testing (retrieval practice), spacing, and interleaving (mixing topics) are all empirically validated ‘desirable difficulties’ that enhance long-term learning. Studying in the exact same environment is actually less effective than varying your study context. Test anxiety is also harmful for learning.

Workout Complete!

Your Score: 0/11

After getting foundational knowledge, now it’s time for you to apply these tips in the courses you are taking (especially the more challenging courses)! Try to slowly integrate these learning practices into your study sessions and let them guide your approach to learning!

If you would like to know more about the science behind this post, I recommend reading the book “Make It Stick: The Science of Successful Learning” (Brown et al. 2014). It is one of my favorite books on learning because it helps both the learner and the teacher. Also, check out the research summary of UCLA’s Bjork Learning and Forgetting Lab, Robert Bjork’s talk at Harvard, and the other references linked below.

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References

1. (Ambrose et al. 2010): Susan A. Ambrose, Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman (2010) How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco, CA: John Wiley & Sons.
2. (Benjamin et al. 1998): Aaron S. Benjamin, Robert A. Bjork, and Bennett L. Schwartz (1998) “The mismeasure of memory: When retrieval fluency is misleading as a metamnemonic index,” Journal of Experimental Psychology: General, 127(1), pp. 55–68.
3. (Bjork and Bjork 2011): Elizabeth Ligon Bjork and Robert A. Bjork (2011) “Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning,” in M. A. Gernsbacher, R. W. Pew, L. M. Hough, and J. R. Pomerantz (eds.) Psychology and the Real World: Essays Illustrating Fundamental Contributions to Society. New York, NY: Worth Publishers, pp. 56–64.
4. (Bjork 1994): Robert A. Bjork (1994) “Memory and metamemory considerations in the training of human beings,” in J. Metcalfe and A. Shimamura (eds.) Metacognition: Knowing about knowing. Cambridge, MA: MIT Press, pp. 185–205.
5. (Bjork and Bjork 1992): Robert A. Bjork and Elizabeth Ligon Bjork (1992) “A new theory of disuse and an old theory of stimulus fluctuation,” From learning processes to cognitive processes: Essays in honor of William K. Estes, 2, pp. 35–67.
6. (Brown et al. 2014): Peter C. Brown, Henry L. Roediger III, and Mark A. McDaniel (2014) Make It Stick: The Science of Successful Learning. Cambridge, MA: Belknap Press of Harvard University Press.
7. (Cepeda et al. 2006): Nicholas J. Cepeda, Harold Pashler, Edward Vul, John T. Wixted, and Doug Rohrer (2006) “Distributed practice in verbal recall tasks: A review and quantitative synthesis,” Psychological Bulletin, 132(3), pp. 354–380.
8. (Dunlosky et al. 2013): J. Dunlosky, K. A. Rawson, E. J. Marsh, M. J. Nathan, and D. T. Willingham (2013) “Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology,” Psychological Science in the Public Interest, 14(1), pp. 4–58.
9. (Dweck 2006): Carol S. Dweck (2006) Mindset: The new psychology of success. New York, NY: Random House.
10. (Glenberg et al. 1982): Arthur M. Glenberg, Alex Cherry Wilkinson, and William Epstein (1982) “The illusion of knowing: Failure in the self-assessment of comprehension,” Memory & Cognition, 10(6), pp. 597–602.
11. (Jacoby 1978): Larry L. Jacoby (1978) “On interpreting the effects of repetition: Solving a problem versus remembering a solution,” Journal of Verbal Learning and Verbal Behavior, 17, pp. 649–667.
12. (Kornell and Bjork 2008): Nate Kornell and Robert A. Bjork (2008) “Learning concepts and categories: Is spacing the ‘enemy of induction’?,” Psychological Science, 19(6), pp. 585–592.
13. (Roediger and Karpicke 2006): Henry L. Roediger and Jeffrey D. Karpicke (2006) “Test-enhanced learning: Taking memory tests improves long-term retention,” Psychological Science, 17, pp. 249–255.
14. (Rohrer and Taylor 2007): Doug Rohrer and Kelli Taylor (2007) “The shuffling of mathematics problems improves learning,” Instructional Science, 35(6), pp. 481–498.
15. (Simon and Bjork 2001): Dominic A. Simon and Robert A. Bjork (2001) “Metacognition in motor learning,” Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(4), pp. 907–912.
16. (Sweller 1988): John Sweller (1988) “Cognitive load during problem solving: Effects on learning,” Cognitive Science, 12(2), pp. 257–285.